{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8fee91a2-2121-44ef-81b3-11ffc68db20d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in the Census tract data\n"
     ]
    }
   ],
   "source": [
    "#THIS FILE: Congressional based on school districts, not munis as building blocks\n",
    "#vs OH_legisl_org, we build each HD from starter muni by contiguous munis based on fractional coverage\n",
    "#this version mostly duplicated from OH_schoolSnap99\n",
    "\n",
    "from shapely.geometry import Point, LineString, Polygon, box\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import ast\n",
    "import time\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/oh_pl2020_vtd.dbf\")  # ***USING VTDs ***\n",
    "tractPopFile.head()  #about 40 sec to load\n",
    "print(\"I read in the Census tract data\")\n",
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly,LW=1):\n",
    "    dummyPoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == dummyPoly.geom_type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y,lw=LW)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y,lw=LW)  \n",
    "def plotCenter(t,geom,FONTSIZE=10):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center',fontsize=FONTSIZE)\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n",
    "\n",
    "def getAdjoiners(UNITLIST, UNITNBRS): #returns all units that neighbor a UNITLIST but are not in the UNITLIST \n",
    "    allNbrs = list()\n",
    "    for U in UNITLIST:\n",
    "        allNbrs = allNbrs + UNITNBRS[U]\n",
    "    adjoiners = list( set(allNbrs).difference(set(UNITLIST)) )\n",
    "    return adjoiners\n",
    "\n",
    "def get2nbrs(ULIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    Get the combined list of first- and second-level neighbors of a LIST, using neighborList connectivity.  Excludes the source list\n",
    "    \"\"\"\n",
    "    firstList =  getAdjoiners(ULIST, UNITNBRS)\n",
    "    secondList = getAdjoiners(firstList, UNITNBRS)\n",
    "    twoLevelList = list( set(firstList + secondList).difference(set(ULIST) ) )\n",
    "    return twoLevelList\n",
    "\n",
    "def getContigFromStarter(starter, VLIST, NEIGHBORLIST):  #all contiguous units in VLIST, starting from starter unit\n",
    "    foundList, newList, prevList = [ starter ], [ starter ], [ starter ]\n",
    "    while len(newList) > 0:\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()        \n",
    "    return foundList   \n",
    "\n",
    "def isContiguous(VLIST,NEIGHBORLIST,returnBiggestPiece=False):\n",
    "    \"\"\"\n",
    "    This method determines if all items in a list form a continuous chain of neighbors based on the passed neighborlist\n",
    "    Empty lists are considered contiguous.  If discontiguous, a less-than-majority piece list is returned,\n",
    "     unless giveBig=True, in which case we return the biggest piece\n",
    "    \"\"\"    \n",
    "    isUnbroken, newList, foundList = True, list(), list()\n",
    "    if len(VLIST) > 0:\n",
    "        foundList, newList, prevList = [ VLIST[0] ], [ VLIST[0] ], [ VLIST[0] ]\n",
    "    while len(newList) > 0:  #this round's list of neighbors\n",
    "        newList = list()\n",
    "        for V in prevList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in VLIST and VV not in foundList:\n",
    "                    newList.append(VV)\n",
    "                    foundList.append(VV)\n",
    "        prevList = newList.copy()      \n",
    "    pickedPieceList = foundList.copy()  #default contiguous sublist to pass back to main\n",
    "    \n",
    "    if len(foundList) < len(VLIST):  #not contiguous\n",
    "        isUnbroken  = False\n",
    "        pieceLists, remainingList = [foundList], list( set(VLIST).difference(set(foundList) ) )\n",
    "        if returnBiggestPiece == True:  #we were asked for the biggest piece, not a random small one, so we must find them all\n",
    "            if len(foundList) < 0.5*len(VLIST):\n",
    "                while len(remainingList) > 0:\n",
    "                    newList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                    pieceLists.append(newList)\n",
    "                    remainingList = list(set(remainingList).difference(set(newList)))\n",
    "                pieceLengths = [len(pL) for pL in pieceLists]\n",
    "                pickedPieceList = pieceLists[ pieceLengths.index(np.max(pieceLengths)) ]\n",
    "            \n",
    "        else: #quickly return a contiguous sub-list that's at most half the total units in the list\n",
    "            if len(foundList) > 0.5*len(VLIST): #pick a different piece; this one is the majority\n",
    "                pickedPieceList = getContigFromStarter(remainingList[0], remainingList, NEIGHBORLIST)\n",
    "                \n",
    "    return isUnbroken, pickedPieceList\n",
    "\n",
    "def enclaveCheck(UNITLIST,UNITNBRS,maxLoops=2):  #TRY 2 FOR OHIO V2.  USUALLY 4\n",
    "    \"\"\"\n",
    "    This method determines if a list of units has an unbroken boundary AND whether its complement has an unbroken boundary\n",
    "    If the complement boundary is broken, there is an enclave or the district is so disconnected its adjoining units aren't contiguous\n",
    "    The method returns contiguity of boundary and of its adjoiners.  Also returns the lists of boundary and complement-boundary units\n",
    "      If either of these is broken, only a contiguous sublist is returned that is guaranteed to be smaller than half (for finding enclaves)\n",
    "      maxLoops is the number of loops for expanding neighbors-of-neighbors for contiguity of the units outside the passed unit-list\n",
    "    \"\"\"\n",
    "    UNITSET = set(UNITLIST)\n",
    "    ADJLIST =  get2nbrs(UNITLIST, UNITNBRS)  #in case direct adjoiners are only queen-adjacent\n",
    "    #adjoinersOfAdjoiners = getAdjoiners(ADJLIST,  UNITNBRS)\n",
    "    #BDRYLIST = list( set(UNITLIST).intersection(set(adjoinersOfAdjoiners)) )  #this might fail for queen-adjacent boundary units\n",
    "    BDRYLIST = list (set(get2nbrs(ADJLIST,UNITNBRS)).intersection(UNITSET) )  #in case inHD boundary is only queen-adjacent\n",
    "    noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)  \n",
    "    unbroken, smallPieceList = isContiguous(BDRYLIST, UNITNBRS)\n",
    "    if not unbroken: #could be that district's boundary intersects the state boundary or there is a nonHD enclave inside the HD\n",
    "        unbroken, smallPieceList = isContiguous(UNITLIST, UNITNBRS)  #slower check for full district, not just its boundary \n",
    "    nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "    while nLoops <= maxLoops and not noEnclave:\n",
    "        nLoops +=1\n",
    "        newSet = set(ADJLIST)\n",
    "        for UU in ADJLIST:\n",
    "            newSet = newSet.union( set(UNITNBRS[UU]).difference(UNITSET) )\n",
    "        ADJLIST = list(newSet)\n",
    "        noEnclave, enclaveList =   isContiguous(ADJLIST, UNITNBRS)\n",
    "    #isJiggy = noEnclave and unbroken\n",
    "    return unbroken, noEnclave, smallPieceList, enclaveList\n",
    "\n",
    "def getBdryNonEdgers(UNITLIST, UNITNBRS): #all in-district boundary units that neighbor a non-district unit\n",
    "    ALLnonHDnbrs = set()\n",
    "    for UUU in UNITLIST:\n",
    "        ALLnonHDnbrs = ALLnonHDnbrs.union(set(UNITNBRS[UUU])).difference(set(UNITLIST))\n",
    "    BdryNonEdgerSet = set()\n",
    "    for UUU in UNITLIST:\n",
    "        if len(set(UNITNBRS[UUU]).intersection(ALLnonHDnbrs)) > 0:\n",
    "            BdryNonEdgerSet.add(UUU)\n",
    "    return list(BdryNonEdgerSet)\n",
    "\n",
    "def getEnclaveLists(UNITLIST, UNITNBRS):\n",
    "    \"\"\"\n",
    "    finds ALL units enclaved by a UNITLIST, parsed into lists of contiguous pieces\n",
    "    We assume the largest contiguous complement to the UNITLIST is not an enclave, but rather the majority of the HD complement\n",
    "    if the UNITLIST's complement (all couldBeEnclaved) is contiguous, the returned list will be blank\n",
    "    \"\"\"\n",
    "    eSets, nU = list(), len(UNITNBRS)\n",
    "    offmapList = list()\n",
    "    for i,L in enumerate(UNITNBRS):\n",
    "        if len(L) == 0:\n",
    "            offmapList.append(i)  #offmap list are units with no neighbors (were surrounded)\n",
    "    complementSet = set([i for i in range(nU)] ).difference( set(UNITLIST + offmapList) )    \n",
    "    remaining2nbrs = get2nbrs(UNITLIST, UNITNBRS)\n",
    "    \n",
    "    isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS) #quicker than contig check on entire complement\n",
    "    while not isContig:  #this will kick out before writing the final sublist = map majority\n",
    "        starter = shortList[0]\n",
    "        newEnclaveList = getContigFromStarter(starter, list(complementSet), UNITNBRS)\n",
    "        eSets.append(set(newEnclaveList))\n",
    "        remaining2nbrs = list(set(remaining2nbrs).difference(set(shortList)) )\n",
    "        isContig, shortList = isContiguous(remaining2nbrs,UNITNBRS)\n",
    "        \n",
    "        # couldBeEnclaved = list( set(couldBeEnclaved).difference(set(newEnclaveList)) )  #revised 18-Feb-24 -- was slower\n",
    "    fused_eSets = set()\n",
    "    for i, eSet in enumerate(eSets):\n",
    "        fused_eSets = fused_eSets.union(eSet)\n",
    "        for j in range(i+1, len(eSets) ) :\n",
    "            eSets[j] = eSets[j].difference(eSet)  #in case contiguity occurs, but not in 2-neighbor list; avoid double-count\n",
    "    remnant_eSet = complementSet.difference(fused_eSets) \n",
    "    eLengths = [len(eSet) for eSet in eSets]\n",
    "    if len(eSets) > 0:\n",
    "        if len(remnant_eSet) < np.max(eLengths):  #exchange the small remnant for the biggest found \"enclave\" (usually the major complement) \n",
    "            eSets[eLengths.index(np.max(eLengths))] = remnant_eSet.copy()\n",
    "    eLists = [list(eSet) for eSet in eSets]\n",
    "    return eLists\n",
    "\n",
    "def wontEnclave(proposedU, dList, NBRLIST, mapBDRYLIST):  #TRY MAXLOOPS = 3 FOR OH REDO, NOT USUAL 6\n",
    "    \"\"\"\n",
    "    This method checks if adding a proposedU to a dList (list of units in a district) will create an \"enclave\" of units in the dList's complement\n",
    "    via two problems:  A) the dList will now have a discontiguous set of units on the map boundary.  The full map boundary list is mapBDRYLIST\n",
    "      B) The non-dList neighbors of dList units aren't contiguous\n",
    "    The method doesn't require that the dList wontEnclave without the proposedU included; it just checks the proposedU + dList combination\n",
    "    It also doesn't check that the dList is itself contiguous with or without proposedU\n",
    "    In that sense, it is more limited than enclaveCheck\n",
    "    \"\"\"\n",
    "    wontEnclave = True\n",
    "    newList, newSet = dList + [proposedU], set(dList + [proposedU])\n",
    "    unitsOnBoundary = list( set(mapBDRYLIST).intersection(newSet) )\n",
    "    wontEnclave, __ = isContiguous(unitsOnBoundary,NBRLIST)  #first check - does district touch MAP boundary in multiple places? (quick FAIL)\n",
    "    if wontEnclave: #slower check below for internal enclaves\n",
    "        adjoiners = get2nbrs(newList, NBRLIST)  #2-level to protect for queen adjacency in boundary        \n",
    "        wontEnclave, __ = isContiguous(adjoiners,NBRLIST)\n",
    "        if not wontEnclave:\n",
    "            nLoops = 1 #could be that fragmented adjoining districts are underestimating true contiguity.  Widen the contig search\n",
    "            maxLoops, ADJLIST = 3, adjoiners #unlike enclaveCheck, here we just arbitrarily set a number of loops for search expansion\n",
    "            while nLoops <= maxLoops and not wontEnclave:\n",
    "                nLoops +=1\n",
    "                adjSet = set(ADJLIST)  #align nomenclature w enclaveCheck\n",
    "                for UU in ADJLIST:\n",
    "                    adjSet = adjSet.union( set(NBRLIST[UU]).difference(set(newList)) )\n",
    "                ADJLIST = list(adjSet)\n",
    "                wontEnclave, __ =   isContiguous(ADJLIST, NBRLIST)\n",
    "        \n",
    "    return wontEnclave\n",
    "\n",
    "def isRookAdj(geo1, geo2):  #intersection with rook (not queen) adjacency\n",
    "    isRookAdj = False\n",
    "    if geo1.intersects(geo2):\n",
    "        if (geo1.intersection(geo2)).geom_type != Point(0,0).geom_type:\n",
    "            isRookAdj = True\n",
    "    return isRookAdj\n",
    "\n",
    "def getWeightedAvgAndSD(LIST, WEIGHTS):\n",
    "    nWeights = len(WEIGHTS)\n",
    "    normWeights = [WEIGHTS[i] / np.sum(WEIGHTS) for i in range(nWeights) ]\n",
    "    AVG = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        AVG += normWeights[i] * value\n",
    "    sumVar = 0.\n",
    "    for i, value in enumerate(LIST):\n",
    "        sumVar += normWeights[i] * (value - AVG)**2\n",
    "    SD = sumVar ** 0.5     #don't need to normalize again since weights were normalized\n",
    "    return AVG, SD\n",
    "\n",
    "def getHDcp(TRACTCP,TRACTPOP, TRACTLIST, SPLITTRACTNO = -777,SPLITTRACTUSE = 1.):  #population centerpoint of a Home District or county (cluster)\n",
    "    cpx, cpy, sumPop = 0.,0., 0.\n",
    "    for tt in TRACTLIST:\n",
    "        USE = 1.\n",
    "        if tt ==    SPLITTRACTNO:\n",
    "            USE =   SPLITTRACTUSE\n",
    "        sumPop += USE*TRACTPOP[tt]\n",
    "        cpx +=    USE*TRACTPOP[tt] * TRACTCP[tt].x\n",
    "        cpy +=    USE*TRACTPOP[tt] * TRACTCP[tt].y\n",
    "    HDCP_ = Point(cpx/sumPop, cpy/sumPop)\n",
    "    return HDCP_\n",
    "\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9e2fbed-247e-4a00-be18-5507b7e2b88f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 8941 popn tracts for OH\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "STATE = \"OH\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "tractCountyNo = tractPopFile['COUNTYFP20']\n",
    "nTracts = len(tractPop)\n",
    "nVTDs = nTracts #nomenclature\n",
    "tractCP = [tractGeom[v].centroid for v in range(nVTDs) ]\n",
    "tractCPx, tractCPy = [tractCP[v].x for v in range(nVTDs)], [tractCP[v].y for v in range(nVTDs)]\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "tractCountyNo = tractCountyNo.to_numpy()\n",
    "countyNo = [0]*nTracts\n",
    "for t in range (0,nTracts) :  #from odd-numbered counties in US Census list to integer list\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "    countyNo[t] = int((int(tractCountyNo[t]) - 1)/2)\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)\n",
    "nCounties = int( np.max(countyNo)+1)\n",
    "skipList = [8197, 4634, 3730, 4458, 840, 2974, 1008]\n",
    "for t in skipList : #coastal vtds for Ohio already known\n",
    "    isSkippedTract[t] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a619e262-3e70-4945-87b1-58245cc514c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 0\n",
      "working on tract 1000\n",
      "working on tract 2000\n",
      "working on tract 3000\n",
      "working on tract 4000\n",
      "working on tract 5000\n",
      "working on tract 6000\n",
      "working on tract 7000\n",
      "working on tract 8000\n"
     ]
    },
    {
     "data": {
      "image/png": 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NTdEsgaCEYOBQi9ddmguH6sCluizNge8T5YjCpblKffxl0c5oXA4X0Y5onJoTTdNQFCWsP5C9JzKZ9+N+rmjkYkzXriGpc+7cudxxxx0sWbKEYcOGcf755+N2uwHQdZ2ff/6ZH3/8kR49elS5To9hvZ3zjOLvYlX0Xra0PwCA+ct/aRffgq8Pr+N4/kFevPjvXNDkDHTTYPzaf5J6/EvmHf2UJb8tYsK59zGszTkhuda6osdf/8GqmU/iKoTeWSYXKr/CZMtddEOPv9N3+P113EOb+sbB9FSmf/E83+j/I8ZI4J5mDzJu8O04q5n5eOTIkTRr1ozrr78+ULZy5coKp1MMVaAOaMIjt5U3/LD4y7yFbHXFVatfVaVt27b88MMPYW0jUrAFliqw9cQB8nI3k5h4Pg2jm6IpDpy+6QKX6iyeLlAduBQXLtVBlG+qwKVaxzgVDZei4VQdRKkO39SCZh2rOohSNGupOXApGtGqA005daRQGaO//JZNumD+VReG4ZuoO2IdltFqgV41u5HKSElJYfbs2fTr1y9Qtn79+sD6woULmTJlSlDCCoBT1bi860P875fneSKtHS2aDGBn4aLA/jW/vUbJGfJxy4ahOFtiGLkII4fOLW/mvORevLvtVZ74agzPJvTl6X6Pclnz0AhptcU7E18m68h6/vi3Bxl7iVX2Xcph4mSxQO9s0LyOemdTH8nMzWbGslf5PH8xilS5qcEo7r/qHhJighcIvv7668C61+sN+nwpLPuUytAkmKeBphTqfDYIsAWWKqH6brg/97iTER0uqOPeVIwmwAyToqOoqIjbbruNHTt2EB0dTdOmTXnjjTfo2LEjAwcOZP/+/SQmWqPn0aNH8+CDoVP9xzgtgaXQMGpcl2ma3HXXXbzyyis8/PDDZR4zZ84c7rzzzmrV/9x5YzHPHV1iSmcy27OOcKwwh315R2nkiiPBEcOCPavIcJ/ArRcR64hjWPvLuLX9+QA80O1qnvlpMYt/mcV9/7uVhIRzmNT3Aa5u1bNafaptju39HwBvTXmLO7t1Y4T5JXG+ef7zz/+AA9EteO6MWOrH1djUNau2fsvfNj5OoZrHoKihPHbV/SQ3bFxn/TEVgVmFmHCKlBin0dRMXV+JLbBUARXrxWPI2olaWBNUIQin5/+4ceO46qqrEELw6quvctdddwVGKy+88ALDhg0LS7t+DUtRCCJHPv/88/Tv359zzil7uuXgwYOsXr2aefPmVbuNk+1Pzm7QAhq04FK6BMoq0po4VY3JvW/h4bOvY8qW+fxv9zwmrLydqQnn8KcedzHqjIvKPTcSuG/eEj548i36XH0hjZ0Kv6w8n/+lJZNjNGfU0iLeGXaca5tXrE63sQF4Zdkc/n30FVrJDrw4ZB6dWrWr6y5ZGhajcp2DCpji9NCwRAL2N1kFVMUSAOqDwKIJETaJPioqiquvvjpgq9KvXz9SUlLC0tbJKIqCahgU1lBg2b59O4sWLWLSpEnlHvPWW28xdOhQGjeuuxGcn3hHFDPOG833tyzjhu5/o8CdzrPf3Evfhbfy0s+fo5s11ziFmuzUNIxCN7c9djN7PviBRW+l8vPBXuQY1hSQkE6+vehCEhz2eMmmfIq8bh5476+8eexFLtQGsWjUBxEhrIBPYJFVnSSJhMmU0wP7iVEFNGF9TYaMvJfDyaiCsGpYSvLSSy+VMlR7/PHHeeKJJ+jWrRvTpk2jQ4cOIW1PkwZFVRjVVERl7o1SSubOncsbb7wRii6HjBjNyd/73MaTvW7mxR2f8/6O//DvjY/xn59eYvAZtzKp5600dMXUdTfJSksl76Xd5AK/rn+bQ53/QBMzFd0UZGrNaC6OMPjxQTji6r6vNpHL7sMp/GXZAxzR9vHHRvfw2PXjq12XYZq88vGvFOZ6LXdj/yPElMWyhCyxbgL4fZRP2ufbjnZLzCrYsNiEFltgqQKqT6NgVlmiLp9ly5YxadIkTNNE13UeffRRRo8eHTIbEKcQ6LVg5PXMM8+we/duVq5cCcC8efNo3bo1Ukpee+01hg4dyo4dO0LapmZK3FWZOK6AitwbAVatWoWu6wwZMqRG7YQLTVF55OyhPNTtaj7Y9x2ztr/NF7+8wBe/vk6jxD70Se7LFa36MrBpZ1yas9ZdoxOaJpOqbCSmSxJnXzqGnUs8HFOagzDp1vQYl0z+Q9B9Mk0TIcRp6aZpU4xpmizatIhXt7/GCSWDeNGIF3q/zmW9+teo3n3p+Tj+l4YDKIi1HBmkAIRPDhEEXJP95SCK10sch7D8QPOTHPTsVoWgbPYtG1JsgaUKqD5vnZpOCUkpuf322/n666/p0aMHKSkpdOnSheHDhwOhsQGJV8CtOZiycTvnN2vMla2a1ai+spgxYwaLFy9mxYoVxMRYI+XWrVsDVryT8ePH88gjj4Q00mJWYRH5ThdFZkFI6iuPOXPmMHbs2DJfqhvSsvnk16O4dQOvYWKYYEiJISWmBN0wMQDT9JdJTP8xpsT0emlQkE7bBC/SNJCGgZp2GLUgn6JWbS2vAymR0rTUzabp25a+UZ7pGxUWf86XnckRzTgatY/j7l2syvyOlTstwTrKiKK9oz1t49oihKB5XHPinHH0a9+P7q27h+X7UxSFrs9cC8DhtduAY5hmFh8kRDPxD5dUKqws+t+LHH7/bToWJdDxkSdQnE68t9zD7ovace2/Pw9Ln23qlrU71/LfX/7LD5k/kKFkoOGgdWZX5o5+PSSGtR6vpRnvcGdnrurbssb1/Z6pa/nLFliqgOaTvo0aju7BeqFnZWUBkJOTQ1JSEi5X6BLRdYyNhmyTN3J13shNIy3EAsvzzz/PBx98wIoVKwIZQnVdJyMjIxAhctGiRSQnJ4c0LPTbP+8EoIkztLdsSfdGgPfff7/M46av38fMpb+Ab0pK+n+5vlGXKLGOf1FinxACPDqaIWne+IDl6qioxBXkA6AXuUFRrOMVBRSBcAiE8MW38cW5UYS1TxEKQhEoikKcELRS+iCEIN9bwKL/vEbqTz/jdCocbnmYveP2sv+9/WRvzcab4eWMKWfQvnV7hrQYwj0D7iExJjGk36mflgO68+cB1sj5+Lo9XNul4ntx8xfv0u3+WXQDoAjj9r/gn4R1VTGgl039YcfhHTy64lEOcACn6aS91p4Hz34QbXtj9v1aRNMGoXl+eHTrua1pdWCyKcRpZcJS15diCyxVQAtoWGpmwyKEYP78+QwfPpzY2FgyMzNZvHgxTqcV8CgUNiC3n9OTK/Lyuey7n9FDfHsdOnSIhx9+mA4dOnDppZcC4HK5WLVqFddccw1utxtFUWjcuDFLly4Nadtu3QQET/brE9J6q8Kk1buY9/lvNGoWy5I7zqdptBOnqqAGGUH3qffX8P5PmfzrkYfC1tcHH3yQKzr35uUv1iKEIC0tjWbNmrHmjDW0bN2SgZcM5A8t/8BP6k+8m/ouCz9cyN0d7+bui+8OW58URWHKRZ0qPMbrKSL6/n8AsGdgRxpecilZq1bSYe1eAFpfPixs/bOpXUzT5I3VbzAnZQ5O6eSRzo9w23m34XJYA7cvfl2PQKDrOg5HzZOGev0Ci1rX+gGbmmILLFVAC5Fbs67rTJ06lcWLF3PxxRezYcMGrrvuOrZt2xZSG5AmcbG0NtzkhNipqVWrVuVaxm/cuDG0jZ2Ev1W/x1Zt8cLGFOZ9uZumreL5elx/Yp3BB/PzoyoCiUBKGRZ7jPz8fObMmcOhQ4cC9TdrZmk1Lr74Yl8fVIafM5y/9/o7Ow7vYPKqyby872U0RWPsRWND1hfTNPnsp89YuWclMY4Ypg6dWuF0kMMZxf4/DqTtvK854+vd8PVuGgFHWkbR4nAR+RlpIeubTd1x4PgBHvr8IXaaO+nu7M7L175M44TS0z6qLzGnrhshElisp4ejLjQsNiHF/gtWAf+D1qyhxmLr1q0cOXIk8PLo27cvrVq1YsuWLafYgOzdu5eMjIzq9xmfsftpgvR997Uprjz1zW5eXPgzcQ1cvPuHc2okrIClHTZ9Aks42LNnD40aNeKZZ57h3HPPZcCAAQGj6LLo1rIbH478kA504IXdL7B82/KQ9MOre7ntvdv4649/ZWXeSv6b+V/GLxqPWcmU6pV/e4PW69fifu2pQFl0roe0h2/j3GtCJ0zZ1A1z1s5h+H+Hk6Kn8NAZD/H+yPdPEVYAFL/AUo0ItGXh9YVCqBuB5fTR6sgIuBRbw1IFVF/gn8oeuJXRunVrUlNT+eWXX+jatSu7d+9mz549dO7cmaNHj4bUBiQC7q2Q4vcgVMLsKZJT5GXCVztZ/t1BpMckqUUsP/z5YjS15g+7cP9NdF1n//79dOvWjenTp7NlyxaGDBnCzz//XG4GWkVRmP+H+QyYN4B/bfwXnZM7075p+xr143jecX4xf0GRCpv+uIl//u+ffJj+IX/84I/MuGYGzSsIyR+X2Jheg26DX2/D4y7AGQGu2jY15/8++j/WFqylo9qRl65+iTaNy7dJ0nx2am8/vu6kH03pX5AU4FY0ayRw8o9L+l+wAsWURAGuMgSWiqJ3hyK3mNWvurb8CB11/V6xBZYq4AiRDUtycjJvvvkmt9xyC4qiYJomr776KklJSVxyySUhtQERnG4aFotwuOmapslLP+xnzncp5B21vJCat0ukb/tGTOjfISTCChTbu4RLw9KmTRsURWHkyJEA9O7dm/bt27Nt27ZyBRaAKEcU/zjvH0z6YRI3f3Yz93a8l7H9y/aUqgrNGzQn2oimUC1ESsnfrvob0V9G8+6hd7lp8U3MuXIOXVp0qbQeW1ip/+w8spObvrwJgCQziY9GfYSmVvza6T2gExmp2ei68IVDkSWW0hcKRXLUa/BdVDTtnRotYqIoOZYpmYBeAFqMRve2Dcpsr7zo3aHILVb3r/jTC1tgqQIq/ki3NX/RjBgxghEjRpxSHmobEOX0Mk4Py0v+9c0HmPvtPjIyCjGLDKIaurj6krZ0aRLP+D6taz2GSU1p3LgxgwYN4osvvuDqq69m37597Nu3j65VyG59+dmX06VZFx5Y9gAv7n2R93e/z5guYxhx3ohKXzAnk56dTqFaSHva49AsG4SHhjzE5QcvZ9yX47h7+d3c1/0+GsY05Nejv7I7czffZX9HC60F7ePa06lRJ4b3Hk5yYvlClk1kcjT7KIu3LOb7I9+z2bsZAIfpYFCDQfzz+n9W6TcV3yCGG8ZdXOlxX+zex/SD2dzXPolL2rWuVn/90bv99OvXjxkzZpxyXE1yi9mEjho9kadPn44QggceeCBQ9uabbzJw4EASEhJKufBWxOTJkwOBofyfLl0qH4HVFqHyEqpNBCBPI+leAoRIaMko9HD+G2v514JtZBd46XpmEo/e2p1fHhvE61edzX3ntg2LsOIfAYZLwwIwc+ZMnn32Wbp3786wYcOYNWsWLVu25J577qFVq1YcOnSIK664go4dO55ybpvGbVg8ajFTe0xFFSr/2vUvBr49kLnfzMXtdVep/e92f8dti29DSMHE/hNL7Tu79dm8fOnLKELh7zv+zoMbH2TWwVl8n/090SKaveZevs36ltf3v87wRcPJLcwNxVdiE2a8upf3vn+P696+jiFLhvD6/tfZ796P03RyRcIVrLx5Jc/e8GzIf1P+QJ6hnCY+OXo3FOcWu/3224Ov8PR5BEcE1dawbNiwgVmzZp2iIisoKODKK6/kyiuvZOLEieWcfSpnnXUWK1asKO6YFjnKn4ANSz3IJeRH8UdqPE2QgAiBzijX4+WS178h70QRI6/sxNMXd6w1TYrwRckMp8DSoUMHvvrqq1PKZ82aVeU6ru99Pdf3vp7Za2bz0d6PeH7P88zcNZMLEi+gb8u+9D+jP+2atAsc79W9rN+zng9/+pA1+WtIkAn869x/cUHHUzObn9v+XL5q+xXf7/keIQTtktrRvGFpm5bHP3mcz7I+Y++xvfRsY+dzjmTe+/49Xt/xOjlqDslmMn9o/gdu7n0zZySfEfa2/b+iUD3mTo7e7SeScovVLXWvs6+WVJCXl8fIkSOZPXs2U6dOLbXPr205OSBXpR3RtIALZqThj3QrI+APVlUEcEx1MnT52kCvz0uI4akLy85QHOlISs9LV5e5Px0h71ghj918Nv93TtuaVxgESphtWELN3Rdb8Vm+3fUt/9n8H77J+YaVeSvBiuFHnBGHgkKukosUEs3UGJo0lElXTCKmAvsTRVG4sNOFgGU/NGftHF7c+yLnOs/l3Gbn8lnWZyQaiXRu3rk2LrNe4DE8SGnya/q3rN3/X37J+IUMdw7RqgOnIrj+jGG0TRzI0ZxMBnYZGHYhfFfqLv664q/sNHfSTm3H38/9O4POGhTWNk/G/zMKhYalrOjdVhs1zS12es3NizoeBVdLYPnzn//MNddcw+DBg08RWKrLb7/9RosWLYiKiuKCCy5g2rRptCknuqXb7Q5Yb4MVMTacBOKwRGBm3PK4omkDDqdlkoElvOyNimdvTiFPVXZihGK95Gv+Y9mWloME7ujZqsZ1VQsJX374LqqqltK2SF9StsYtWtJv8OV107dy6H9mf/qf2R/TNPk19VdW/7aarw59hcvhooGzAS3jWtItuRuDuw0OCCrHc4/zydZPOJRziGvPupYCdwHPfv8se7ECwUUb0TQQDfDgIUOx3Pc3ejay8YBly3VBwwuIckTVzQVHCAWeLDYe/JSPdy3gy/R9gXINSauoKJpGJXC4IIMMr8G6jf8B/gNA3Pdx9Invw8B2A+nWvBtnNjszYEtUHoXuQn5J/YUjWUfIdedS5C0ixhlDl+QudGnRBZfDhdvr5ptd3zDvp3lsdm8myozivo73cedFd9aJvZd/SqimT4Wyonf7qWlusSTvUcZmfU7+jNnF4a+F/6MAVsRqK3eREtgn/OtYka2t8xTr499WVF+Zb6mo1jqUCLVdEkEpy2T//jIFPn/mR3ySoUQIAwiNq3l1CVpg+fDDD9m8eTMbNmwIWSfOP/983nrrLTp37kxqaipTpkxhwIABbN++nfj4+FOOnzZtGlOmTAlZ+5XhD1ZW0zgstcndvc+mZOzSsSu+ZX0lP23pz1sDpyxLrvvdu0vt8/kd+7VQskSG0/LqDJwvy9pXPP0mpcRd5AFqlsIg1+Nl/Z4MFJdKlFazmCrVQboLAfj1s8XlHvOrEJx/2WArPH+E4TW8nMg7Qe9WvenRoge6qWOYBoZp4DW8fP3r16TmpLIpbRPr89bjUTwALFy9EIAGRgNQoYvShQ6JHdiWtY14JZ6/dPsLi3YuYpu+LdDW2sy1pKVuYd43k7mt32O0bl2zBHj1gWM5v7F699tsOLqRHTnHOFjkxijxmz0vIZZbut5J/3bDiYsqDnkgpWR72lre+248hZ5E8unMhtwNrNmxBnaAIhUSzAQaa41pEd2Ctglt8ZgeDuQcILUolQwjg1wlt+w3/y/WqDrRTCRf5ONVvEQb0UQviSb1p1TGHRhH3y196dWrFxkZGQwaVKxlKSgoYO/evaSnp9OoUaOQf1+BKaEaSCzlRe/2ewhVlFusKlzg/p7+xz5Geqzzi7vq630Jt+fSskQJYayk91MdPhbUVi1wyfS66wBBCiwHDx7k/vvv58svvyQqKnSjn6uuuiqw3qNHD84//3zatm3LggULyrTMnjhxIg89VBzePCcnJxB4LRxoPqk1FLmE6oroApVYxcH+x1cjEGXI3pFt8NK4jQP1zOr9WjOLvFzz1noO789GSOh2dpMQ965qxEZHAwWM+MfzNGzewpdjSAEsQXHlRx/y6+cf4y4qJComtk76WB4px1K4/b+3k61mV3psnBHHubHncv9F93PrilsD5S9d+iJ92pc9JfnFni9Ah5HJI3nv6Hs0Vhvz9y/vZa3MZ97Ke1h59XyaND0rZNdTV0gpOZL+P5IbX0yR7mFdykLWHljO5ow9HHR7kAgSVcnZic25um0Pzms5mO4tLsOpli+sCyHo3vxiLomOR0a34eqr/o1u6Pxy5Bd2pO5gT8Ye9ufuJ7Uwla25W1mbvxaBJYQ0cTThnNhzaJvQlk5NOtG6YWsaxjQkyhlFTkEOO1J3sPPYTvbl7KORqxGXtL+EgV0H8kPHH+jQoQMXXXRRoB9JSUls3bo1sD1jxgxWr14dFmEFQmN0W1H0big/t1hV0RokciinDW0n/1SjevzJUEstDR1MAwwv0jTB1K0EqdIs7aAQGBj631+yxP4SWhRppT8ppQkquVw2FK1J6PLDVYegBJZNmzaRnp5Onz7F+VwMw2DNmjW8+uqruN1uVLXmI9cGDRpw5plnsnv37jL3u1yukCYMrAzVb3tQjyObJDrjkIaH3F7OQD4uf1yQssLEB8rEydvFx/qlf3GSarF0daLUMYF9Jes5uS3EKeVmjqdaLm2maXLDhxs5ciCbQRe25uozm3JT57qxlZK+KcW4Bg2Ijo07Zb8rztImFuXnR5TAYpomD37+IG7h5unuTxPjjEEVKpqqoSkaDtURsPNqEt+k3KBgo9eM4fJvGzBhyBs0TT671L6ZN8/Ea3hxOVxctucyWie15qdt/2bt3gVIIbjus1tZePl/aNnyvLBfbzjp8U6xk4JAIhEkqIKeDVpwc6fzubj9rbRv2LV6qRuEB0VYz0VN1ejeunuZWbmLvEVoilapu3rzBs3p3KJsOyJ/tO6KmDNnDtOmTatCx6tHqI1uw4FEhMRZQAgBJd6t1jXX3jsQsKas6pigBJZBgwaxbdu2UmVjx46lS5cuTJgwISTCClhGvXv27OGPf/xjSOqrKcXGknXckRqgqSpCCs6+7VTPjfpA1JrfwJNXpWPn/nSI6f/dgSlBNyWyQOeGge144cq6HaGbhoFEoJZjT+D0aS3XfLyQqLj4wBSdNb1WHDTLP6ds3Y/Wfr9m2TB0FEVFc7mKH5Nl3LiBKbdSu4rrLHnqvvyD7I7fTQfRipSDPzLsvD/QrnnFyQz93OBMZonnaGD7f0YW/1s+gptcLfjbjZ+g+exUFEXhw8/H4zW9CAR3ntiIKiVPt76GJw4tI08R/OF/d/Cvnvdxfp9xVWo7UjBNky92vsk7O+aVKr+n02Vc1HYo3ZsPDokNiJQmmuqs9LjasA1at24dmZmZDB06NGxtBPKLRcCLtCJEfX5xnERda+KDElji4+M5++zSI6PY2FiSkpIC5WlpaaSlpQW0I9u2bSM+Pp42bdoEVIODBg3ihhtuYPz48QA88sgjXHvttbRt25YjR47w1FNPoapqmQHW6gL/aKc+2bCcTH3Pci4lKFW4gL3ZBUz/YidFeV7OPKsxMVJwebdk/lzLHkFlIX05TdRyEro1btkaieC3FcuCq7fEejgeJ3lROuolgr3qIfZmLSR11RH+ObJiN2lpmqzb9DobC4+Cbxyz5Q8b2L33f6zduZDXT2zmwHsXMarLH2iW1JlZ30/nSzMrcH47Q5CiwhOHrO+ioyFIUqO4+6eXGbd/FfcM/Q8OR2RHwjVMgyXbZjB7x4cc8ei0djn4a89bubn7Y1USLIJFCBOh1L5tVlnMmTOHUaNGhTU8hX9KaOuJLNomNSQpqnY1DlUizKlEfm+E/G6aOXNmKYNYv+pw7ty5jBkzBrCStB0/fjxwzKFDhxgxYgQZGRk0adKEiy66iO+//54mTerG1qAs/Fl26ytWILn6TXk/fVNKFvyaxqx1+9i3Nwuk5NoBbXn16rPLOaNuME0DhChXYOnWuw/t5ryPoesIRUHxeQooioJQFJ8DgVI8xVbGw9A0DUyjpDdb6ek1USLbdWDqrcTovrxpwklSoqoq/Wb3QXGcOqLNOL6Lfy6/mw2eDBoJlTxpckSF9gjuSejGvde+g6o56dL5Orp0vo6uP7zCyz/PYfzOuQBoUgau57NL36BNm4vIPLGHVZteRxoq/35xA2v2HqLAe5yJMSl8urMPz177NP/95DfefvttfvvtNxYvXsywYcMq+hOEHSklGw8tY/GO2Ww4sZ+jHp3WDoMnev2Rm7pPCLM3jURQ9wJLXl4eCxYsCKljRlnEOazX11+PF/Hy4U1svfLCsLZXHeRp8eSNHGossJwcb2Xy5MlMnjy5wnNSUlJKbX/44Yc17Uat8NWJHB74Zb/PHdUq8ynnA8n5/M5gfum/hHOYpdVHBs6Xp+wvjvRSclncliyn/KR9svRPRAIZhhtDShbM3UxDTbP2i+L9QCDQnH87qUsS553TsqpfT1g52ak5Lc/NKxtSWLPnOIeP5GEW6IholfN6N+Mfg7rQqWHVR9/Lli1j0qRJmKaJrus8+uijjB49GiklU6ZM4f3338flctG4ceMyg7JVFa9XR5EGWgUupjFxp3rFVZWKvDRmzpwZkhe7IUy+zl/P5bMuwhAmUkiOO/IC986ZmQ6ivYLCaMGIg07+eP0EWg+48ZR6LjrvL/Q/98/s3beSEzkHOKvTtcTENS11TMNGZ3DjkOcoKioixlwVyPfytyfuYvZbH/KH5n/nQjOWxQvn8ee/PFat66kK6Ue3s3DdPygkj+16GnkIdGmiKSpOoaAqKopQyPAUcdRdRKHvYXBhw6Y82XcMA864vXo2KUFj1q0biY/58+fTs2fPsEcrv7xDW/6Ws4N/ZOukRXDeKVvHEjoiJ5xsPeCEV+fDtMywtxPMDV7esSeXO1RJ3yyDJoc9OP2j6BLH+W0ghE80aFBkkJpaABEisHgMg8KMIsYv/5lmiVH853+/YRYaOBOddGrXgKFdkrmnTxucQaaQl1Jy++238/XXX9OjRw9SUlLo0qULw4cP5z//+Q8//fQT27dvx+l0kpaWVrNr0E1UaYbNZbkiL43Bgwdz2223cccdd9SojWEJg9mTsxdFEagoCARd8wopzD9I/xONaYYvGmgm7DmUzf51K8oUWMDS7JxxxhAqi4l6cr6XG67/E+/N+5KR0e14r3UK6zbfSUHqPvKyDtbo2kpSUHCcZd8+w5dHvuE7WYAUglhp0ixK0iy2JZriQDd1PKaObhp4TZP2sQ25qEkTOjTsxqBOo2gUW7uxfoSQiJplW6ky99xzD5999hlpaWlcccUVxMfHB8wA5syZw913311JDTXnv7/t4x/ZetjbqRGn0ZSQLPF/XWELLFVGcG2TRJ47vwcK1gte8eU9Oh1Z9u/NxGV6gjrn8ssvJy0tDUVRiI+P5+WXX6Z3796B/XPnzuWOO+5gyZIlQY/wUw7m4vzlBJ9ywipQBc+OOYebu9Tc46dkzqucnBySkpJwuVw8++yzrFq1CqfTsjeoaSRmr26g1KKnWUkvjfPOC413zd9uOTUxXHm8NfZKPIX5IWm3JFa+l2E8fstL3H7oe/68/A5WCslTm55lhZjH9S0vJnFdP07sOMyw/9yDFlN1I9O9+1axYMMLLM3fR56A80Q0f212KVec9wC5P/2dPd61XDrky5BfU2gwEaJ2HukVpXpYt25drfQhq8gKHvpS02gubt2iVtoMFitC9+kzJVTXbztbYKkyAiHAWUsBve677z6WLl3K/v372bJlC7169QJg+fLlTJo0CY/HQ0xMDLNmzaJnz9DnW5EClCDfrQsWLAhEilyyZAljxozhxx9/BKxpwNmzZ9OvX79q9aezVPkS2Pz3yzmU46ZRtIOmsTU3shNCMH/+fIYPH05sbCyZmZksXryYoqIijh49yieffMLChVbgs4ceeohbb721khrLx60bqLUksNSGl0ZlOB0KnqKqJU2sKifne2nVqh+f3LWDi2b35NzEprjJ4clDX/CnI1dDg0Ysm/8C1/zxYVStfCPXY+k/s/anuSw/vIbvKKSRKbk1oQs39XuslBt1gRaLaVhauUgcqAghI8botjbwe7r1bdmM5gnVn0oNKwGttU0osAWWIKhNo9ubbrqJxx57rFRgpszMTEaOHMmaNWs466yzWLt2LSNHjmT79u0hb18qAiXI6y0Z1jo7O7vYu8o0ueuuu3jllVd4+OGHq9khcCGIczro0rjiMOPBoOs6U6dOZfHixVx88cVs2LCB6667jh9//BFd1yksLGT9+vWkpKRw4YUX0qVLl2oLiF7DRKslgaU2vDQqw+FQ8XpCF8q7vHwvAFp0Qwae+2eGDRvGjt2fs/TwJxwkj5nKD7z2zvsMjGtLr+RzaZPcA5cjjmNZe9l8YDXrsnbyk+JFkZLeRPGPNtdyZf+JOF2nvgAVLQY8AqkXIiLSQ8lEqSUNSyRgmv4IsZEnPPqRIe5bRXZqV111VSBlja7r/Pzzz/z444+nJCiuz/x+7u4aU7vW3mUFZtqzZw9JSUmcdZYVT2TAgAEcOHCAzZs3lwrmFwqkEFVyIz6ZUaNGBQxTly2zXFKff/55+vfvzznnVD/x4mdHMskNw/e/detWjhw5Evi++/btS6tWrfjxxx+Ji4sLpJRv164d/fv3Z8OGDdUXWHQTtRbuodry0qgMp1PDEyKBpaJ8LyfTreNVdPvbVUjTZNX3z7Jm/wrW5O3n/cL9kLIocFwDU3KOlsg/Wl5J/x5jSGp8ZoX1KpoVzM9wn0CJQIFFCImi/H4e6f4BpBIBhsblEarAcX4qslPzpxMAWLhwIVOmTDmthBWgliy0TgvqXorv1KkTGRkZgTnipUuXkpube4rXVSiornj2zjvvcPDgQaZOncqECRPYvn07ixYtYtKkSTXqT1yYcv+0bt2a1NRUfvnlFwB2797Nnj176Ny5MyNGjGD58uUAnDhxgh9++KFGDwCvYaKGIuV0JdSWl0ZlOJ0OPN6aJwz153vJysri0ksvpVevXpx//vkATJ06lVatWvHdd99x11130apVK44dOwZYRr2DLpzAlBFf8vkd21h73VI+PG8y886ZyCcXv8TqUVt58Y/fct1lz1QqrACojgQAzKITNbqeuXPnIoTg448/BqwX7+TJkznzzDPp3r17IKdNMEgpURQDRQl9fJdIRfpSpURi3q3aYs6cOWWmrymvvCZEwsTW70ccrymidqeEyiIxMZGFCxcyceJE8vLyuOCCC+jWrVtY1P41Fc9Gjx7Nn/70Jz755BNSUlLo1MmKjJqWlsa4ceNITU3l3nvvrXJ9A5ok8FtmQQ17dSrJycm8+eab3HLLLSiKgmmavPrqq7Rp04Zp06YxduxYXn/9dQAmTJhQI+NVr2Gi1oLgW5aXxtSpU5k5cybHjh1j+/btjB8/ni1btoQ11pHT5cTrrfkUWEX5XiZNmlRlYbhBw/Y0aNi+2v1QnJbAYrir7ylYli3Xyy+/XGNvNMPwIoQkL/8bNm6caU2TCMs9QAhheQ/5ljExDenU6RpfHqva4cvNv7FuVxqtG8WiKsL3UQLrmqqgKAJNUQLlmqqgqgoOVcGpKThUFVVR0Hzr2XlucGvszCkiX5e4hMCpKLhUBZdi+UuJMtOC+BeilBOP9T1R4tiyf6tl3YlSSsySYSl86x5TlnNGzSnPTu3gwYOsXr2aefPmlXNm9alXkW5/78gIkDEvvfTSwAjM7XbTrFkzunXrFvJ2gtWwZGVlUVBQQIsWlrX+xx9/TFJSEn/961/529/+Fjhu4MCBPPDAA0F7CUkhw/ZTGTFiRJlRlZOSkli6dGnI2vEakiC9rqtFWV4awbzYQ4UjKgpPhHudBoPmbACA4amewFKeLVcovNGEEHi90Tgcv5Kd82uFx2acgAYNk2napPbyMt29YJdvLSek9bqAu7+uWbiBqlDqWVji4VjZM+kh7RhnOWquZSyL8uzU3nrrLYYOHUrjxo3D0Krt1lxPEBEhsKSmptK8eXMAnn76aS677DI6duwYlraCudrs7GxuvvlmCgsLURSFJk2a8Omnn0a0QVxt4zUk2u/o+3BGReEJz7O6TlCdDYHqa1jKsuXKyckJiTeaqjoYdNlGPJ4CpDSR0gAkpmn6tk1AsmfPt2TnPIGuBxeyoKb88awo5v1cxKXNDZ685UJ0w8QwTHTTWhqmtMqk6dsnMUxrXTclHt0IHKP7zvtqTy6rU/K59eJ2JDWIQjclXgle08RbInqmPz1Wyee3LLEiKR1ps7QyT5ba9ufa8keaLk4Aa2FFoy6Oc5W0OQq1IPTvjfLs1KSUzJ07lzfeeCPkbUYCtsBSZUStJj8sLzDTk08+ydq1a9F1nQsuuIA5c+aEpf1gFQFt27blhx9+qPS4kyMjV7k/QtTjXNkWXlOi/X7kFZzRsXiN08e+QI2ycqEZnuygz/Xbcq1Zs6ZUua7rIfNGcziicFSS2DAq6leyc4BaDuF/64CzmPfzJmKjHLRvHpqRf47yG6tTdjGye0t6tG4QkjpDzdrd0RD6mexy7dRWrVqFrusMGTIk9I1GALbAEgS1qWEpLzDT7Nmza60Pda9PKkZVBEZE9Sh4vKakjDQ8py3OmFh0qWB63CjOCExMFyRqVBIAhjf4aY21a9eWacs1ZcqUkHujVYQQhm9ZuwLLWW2Tae50s2K/FrI4NgG7pkgeBAglLHFYyosmPGfOHMaOHRuWnFWR8PT9HT0+a0r9Tn4YLJGWsksTgvo+u+A1+V0JLI6YOAA8OccrObJ+oLp8AoueG/S59957L6mpqaSkpJCSkkK/fv148803uffee0PujVYxfj1l7d6IQgj6tYmjyFRpP3EZhe6au7v7n09KBAssJdOfhJJ169YxduzYU8rff//9UsmHQ01df9W2hqXK1PWfqnYRWIaukYKm1n+BRTch+ncksBhOK/jaP9/4N2pMLJoicKiWJ4imCsvrw+cFoqkqqqoiFGupaBqKoqGoKorqQFU1hKqiKgJFKChCWPmMfJmsVUVBUSyvElVRUTXV521itaGqaqBcESqKYnmlKP76FGHVKfCVK752sEarQiB8kVWlXhjS7ynU3mgVIf2/Iln7EXH/Ofoyvp3yMel6NOdNXspPzwyvkaalPowfpYisgV99xxZYgiASjG5ri0gTzzRFqfcCi1daL+zfC6lRHQD4LKsdcV4NXQp0qeD1LXWpoGMtvZU+igzfp25QMFExeX2IYHuWSk0z15S05QqVN1pm5jEKC7MxTQMpDUzTwDRNwL9ukH40hezsJuzatZ8jR0yfUa4MLEuul7WvrGN3ZoER1QCEwDCl9ZES05QYEt/SKu/foRFLdhWSK6M4fCyTVk0bVft6iwPHRfJvKjxTQr9XbIGlqojf15QQWLbwkYKmWj97QzdRa8M3OAzoUuBU62ffq0NMQ8u48rmbujPggsojMUvfS82UYBoGpuHF8BRhGl6k7sHQPZhSWvukxDAkUpoYhoEpTUzDwDANdN3ENA10w1r6vU1M31JKE9Pfji9+hinBNMGk+EUrIXCcIf2xNhSOOyMjg3lJjh3by2uvvVPFo68Etvo+NcOU8I67L5AFWL6UAonie3r4twW+SLyAhkq8cGPW8Pnii8wf0QJLBHetetTxK9AWWKqKBI9HJyunKKDG9AcHkmAFlsNSUwaCB5Xwq/OXlwosJEsfF9j2ByDy/SclSBMwfevSREho0SSWqKjQ5dUpiRJhViyKbqnjvV6j3gosXinQPW62/bA+8CQ7OXBV6SecFfTLP31hlVjZX6Vpous6pmGg6wa64UX36ni9Xp/nibU0dCPw0jZ9L3DTtw5Q4PWSeiKL9s2a4tRUhKJwfH8KUbGxNGzWHOGbLhGiuC8CaypFKIovOJe1LhSBgu8YReHIoVRMh5P8vKpNoQgh0PwaKE0BHBATWSHwP//yrxgR+BLKyT0CQP+LGtIsuY31NxAKQlFRhIIQKoqiIoRCfr6JpjXzHSNKLcsrK/kpWZ5f5OGd577nvvMSGT/0vMB9cvInHJh+DUskG7FwemVrrmtsgaWKxBjQa9MJ8r4MX36WkwIxVsr6llFc8pe+YemLaSVGD0vd1cE8Zr309CIdosMjpIUbt+Ig21PIomWf137j0icuB+JIWGVStR4Buw6ngmkQCCqRU8C+3L01b7djDzLzgjdSjVRMqSJl6BI6hgrdWwTAGR360aHD+bXWrlfmA+ByOgKB72oLvxygRrIaI4LzHFWHuv6qbYGliqhAYZKLfRe2OeVVLsRJ25TxqvcFGSoZ8blkmGP/fR0IIw3Fhvy+BoQQSCEQCuj/3YdSGMYwopElrxDTKgGOHMeo619MDZCqRpOGidw8sHtxWakAVyetI5GmfyrCLNbACSt/iqqqqKpmLTUNp9OJw/dxuVy+dZdlaKqoQY10S9krmCaGafg0NL5pFsMq90/FmKYspb0xpUl2XgFLFi/C44wN0TdY95iomDLywvd6dUtg0bTa1UjpPk2dWgdaDr9muiZtu91uHn74Yb744guioqLo2bMn7777brnlweLTxVe7fzalsQWWKiIQJDRwMeCitnXdFQBWf3OYZmlFYavflBElr6BGWV4NhlF/w8cZUpCYEMdZ59ZeSPTqUnIKCKA6Oi2vbrBk8SKK3LUbVTWcmFLBNCNPYNG9bgAczuhabddrWIbQWh0kIDR9Riw1GcM8/vjjCCHYtWsXQohAHqfyyqtDJD1H6zu2wFJF6jrp08mccdCaItmbX2QlEBMCTQhUQal1TQhUrPWgRtgRNirw2zZ49forsPzejG4dmoouFYrc7pDWe/nll5OWloaiKMTHx/Pyyy/Tu3dvli1bxqRJkyzjWl3n0UcfZfTo0SFtW6L6wt5HFobhE1i0iiPdhr7dutSwWFT32Zyfn8+cOXM4dOhQ4NnYrFmzcsurRZgCx9UJEfAKtAWWKhJBIUkA2N0+lo778nE+vQHLcbHY6VOedGNJwCFhVzMXlz1QtdG9Ket+vrIkqu9FbxoR9ocIAkMKHL8jgQXAECruEGtYFixYQIMGDQBYsmQJY8aMYevWrdx+++18/fXX9OjRg5SUFLp06cLw4cOJj48PWduWDUvtaVgMw0tGRkqpsrK8FbOyrOB8mla7GpYir/XUKapLGa6aD+c9e/bQqFEjnnnmGVasWEF0dDSTJ0+mSZMmZZYPGjSoGn2rVtcikkh48toCS5URERVI7ZzbupLyYzqmiWVLgCWwSGnFPJCSYrdNJA2+O0rTE1V/cVgPxcj5tflHcPVaw4JCVJhD3WZkZJR6sBYUFLB3717S09N5+OGH2bRpE4qi4HA4mD59evUewkFgCBWvJ7QaFr+wAlbSzUCyOSHIysoCrKSCSUlJuFyhTglQuwLL/PnPsGtXVZ87Eperdm1Y0nOtaemdx6o2PV3R/dmwYUOmTJnC+++/j8vlonHjxnz11Vfl1uV3a66udkfXdfbv30+3bt2YPn06W7ZsYciQIXz22Wdllv/8888kJycH1YaIMG/L+o4tsFSRUN52ZRl0/fvf/+a2225jx44dREdH07RpU954441yMzHHJ0bT/eKq29Msy3bTYkfVk7bJCLNh8U8JGfVUYPEaJhKBK8wu2UlJSWzdujWwPWPGDFavXk2jRo144YUXAi/7LVu2MGjQII4fPx6WvCN+pFDxekPvVTNq1KjAy2zZsmUIIZg/fz7Dhw8nNjaWzMxMFi9eHHLPFUOqKKL27sH8/CISEgwGDuwXKCtvajchoRnR0ZaGpV27drhcrsD2xIkTufXWW7nvvvtYunQp+/fvZ8uWLfTq1atG/ZNFeQC0cFQtw19F9+dLL73ETz/9xPbt23E6nZXajQTcmqupCm7TxnL/HjlyJAC9e/emffv2HDx4sMzybdu2BS2wBDI624QEW2AJitCILOUZdI0bN46rrroKIQSvvvoqd911V7WzG5+MkNZUkds0UXw2LRX90P2xZWqKLBWYpvS6LKvcLBGTpjhQDYpurR84eBTVzAesgF6B+DZSlrK7EVhh1oXAFyvEuhx/+HVKrAssr5tAqHZFRSgCUSL0uxV3RLWmyXyh2lEUnzdXie1yvtNCn+o82lG7IdHnzJnDtGnTgFM1E7WCoqGHQWB55x0rSNrbb7/NhAkTWLp0KVOnTmXx4sVcfPHFbNiwgeuuu45t27bRuHFosgODZXSrKbU3/2EYJjExCn36DA/63Pnz558ikNx000089thjXHTRRSHpn1+D1bJxg2qdX/L+fPbZZ1m1alVAyKzMbsQvsFTXrblx48YMGjSIL774gquvvpp9+/axb98+LrjggjLLu3btWq12bEKHLbBUEYEIiSFqRQZdV199deC4fv36MWPGjBq35+eYPMRzLZ/lyPt+dbbPwr7EesntGE3iaiThPyX3+oSJwJJS26WPocxjA/vFyeeefETpc/WiFsD/MfrzPVW42vAhpBl8ICghMH1+686M9DD0qmzWrVtHZmYmQ4cODZQ9/vjjfPTRR2RmZrJo0aKwalcAUDUMPXxxS0aPHs2f/vQnNm3axJEjR7j44osB6Nu3L61atQqo80OFWctGt4YhcTpD9zfyfz+hQvHF8UmIjwv63JL3Z05ODkePHuWTTz5h4cKFADz00EPceuutlfehBga/M2fO5M4772TChAkoisKsWbNo2bJlueXBIoQ4fYxuIwBbYKkiAnGKMWt1KM/Q62Rbgpdeeonrr7++5g36cDnTOepIZ0DrESiKNYKRvp+SlCB9qQekLyu1s9CgTZ5JvMO6RaxHZolIrKLYOj+gqfHHi/HHkjnlWGGdLkD4azxpO3BsCa8mgeBAxi7ek7N4SF5Nq+QzA1oSUdysVSaskMMmvtDqvmeFFX5dBKINmwHNjBUkT/pyn5imLwKK6SuTfk2O6Qvfrvi0P75pgYCKx5cYT/rVPrJ4v08tvOboEbrK2vPimDNnDqNGjULTin/m06dPZ/r06axYsYLHHnuMb7/9NqwBvxRVwzRCJ7BkZWVRUFBAixZWNp+PP/6YpKQk2rVrR2pqKr/88gtdu3Zl9+7d7Nmzh86dO4esbQutlo1uqXZk51GjRiGl5LzzzmP69Ok0adIkxL2zpjqheMo2GEren1Z0Zp3CwkLWr19PSkoKF154IV26dKFnz55lnu/XsNTksdyhQ4cy7WTKKw8WeRrZsETCVdgCS1UJ0V+rPEOvkgZdzzzzDLt372blypWhaRQC8+4v9n+Q6Fr2JAgF6zd+wvz8z7mwSxvOOv+yuu5O0Hi9Xg7/4x+4RO3kocnLy2PBggVs2FB2ZObBgwczfvx4tm3bxjnnnBO2fiiaA6MoP2T1ZWdnc/PNN1NYWIiiKDRp0oRPP/2U5ORk3nzzTW655RYURcE0TV599VXatGkTsrYt1JDEYcnJyeD48YOUkvVLGPX7szS43QYNGgQv5K5Zs4Y2bdrg9XqZNGkSo0ePZtmyZTXu98nouqVtcqjBTXWefH82atSIuLg4br/9dsCywenfvz8bNmwoX2DxjRnqwqW6qkSSp2UoqOvLsQWWKuIf9deU8gy9/AZdM2bMYPHixaxYsYKYEOZR0bEesg6lfoa192ttIkHKrw7hyqdSHvPnz6dnz5506dIFsASm/fv3B4y4f/jhB9LT0+nQoUNY+6FpDowQBlpr27YtP/zwQ5n7RowYwYgRI0LWVpkIDWTN3bT//e9/kZNTlYFDPNHVSEXhF9QcDgcPPPAAZ555ZtB1VIViDUtwWqCT70+w/n7Lly/n//7v/zhx4gQ//PADjz76aLl1+J8FkZz8MC8nlwbk4X4yCaDC6SERCC8ucSjFht1eUz3lLFnyeShLPhtLtnBya6LcY8RJdZqGgfTVq2iadVS7RApzciq/6DBiCyxVRFB2/INgKc/Qq2vXrjz//PN88MEHrFixopSBZCjw+vKfqKJ2jT5DRWDKSNZPLyE/tdX7OXPmcPfddwe2vV4vo0ePJjs7G03TiI2NZeHChTRs2DCs/dAcDtxm5AVaqy4e2QiXcrzm9XigfXsP5/cbUJyOwWeILn2ZVKXP8v2MM/pVVNUp5Ofn4/V6A8+QDz74gN69e9e4z2Wh++ZcHUFOW518fwJMmzaNsWPH8vrrrwMwYcIEzjuv/LhRAaPbCNawbKQPG9xF/PGM4jB3JZfFZgalr8HpyaRB1iYyGl2IofoEW38+MP9Ek/Sv+50WfPtliWVZ5SWPL1UmfbsknoJ8Mo8cIql1WxwuFyDwyt84LkNnwF4dbIGlyoTG6BbKNvSSUvLwww/ToUMHLr30UsCywF+/fn1I2tSlgUNqtT7SDzX1VcMSMG6tpcyt69atK7UdExPDt99+Wyttl8ThcKJEYCj76uLQGiI9+2pcj2kKGjRoSJfOV4WgV6U5evQoN954I4ZhIKWkQ4cOAa+qe+65h88++4y0tDSuuOIK4uPj2b17d7Xb8urVC81/8v0Jlsvz0qVLq16J76cUyU+0Aq0BC4wr+etdN1Tr/NqZQK4a+pxz8CrBG1eHEltgqSKh8hKC8g26QqHBKQ8DHVXWT+0KFD+UZD3VsPgFxfoqcFUXp9OJwumjYXE54zH0whrXY5oKmhae6dkOHTqwZcuWMvfNmjUrpG35kx86tNp/tgTisERw8OjTx+TWT92KhxH8p448amlwHBY8Ukej5g8Vt9vN+PHj6dSpE927dw8YyS1btow+ffrQq1cvzj77bN5+++0at1WS4hd+/fwjBPpfn2+iauByOdHqqZBZFooaR5Ra86SjpqmgqvXTnqwkfhuWukg5Yfino4I0+K1NwqHQnjt3LkIIPv74YwA2bNhA//796dmzJ7169WLVqlWhbzRCsDUsVaS+j48NqaOF4M9dVtA7KWX487jU86ksAGGa9VrorQ5RLheKkBS6vUS76v8LWlVjiVKL8OgGzmpqFUxTD6uGpTYJCA11oGHxtx3JNixCiIAxayhISUlh9uzZ9Otn2TVJKbnhhht46623GDx4MLt27WLw4MHs3LkzEOX4dMIWWKpIfQ+w7JU6Wg2nhMoLeielDHsel+IpoZq/8cvL9hvqsOVloR8/FvI6I5mYKCvGS05eYUQLLCPnfsnhrFzaN/f4LCFL/t79UQ4FWt4xbuto8vn6EQhHPAgFUECovqWC8K0LoSGEihAqitAQim9pSkBBrYOXfKjxGtZ0X10IDUY9kP5D+a2Ypsldd93FK6+8wsMPPwxYuZmOHTvG4MGDATjzzDNp0KABn3/+OcOHBx8dOdKxBZYgqK/TEQB6CKaEKgp6F+48LqGcEior2++PP/4Y8rDlJyOkpGhfSljqjlSioyyhNbugkOSkhDruTfl8u9MDuDhw3PBl/z01AjNImsckcaJ1PI3YRFa+FepAAVSsOCo+8QVFFC9PxjA0YARHirLCeEW1g27UnYalHsgrKCHUsDz//PP079+/VNykxo0b07x5cxYsWMAtt9zChg0b2LlzJykpKSFpsySR8P6zBZYqUt8DLOvSQK2hwFJR0Ltw53EJxGEJwR+hvGy/oQ5bXhYx5/QJexuRRGy0FfQsN7/mdh/hJCHhGI0TJKvGj63C0fdVqU5TmuimF8P0ohseTKmjmx6OZ6ez7tulEFVxrpz6QMDotg7sSPxTQhGNICQR0rdv386iRYtYs2bNKfs++eQTJkyYwLRp0zjrrLO46KKLSkW3DiV1Pc9QI0up6dOnI4TggQceCJS9+eabDBw4kISEhFLTBJXx2muv0a5dO6Kiojj//PPLDQ5VVwhEvfVQAUs6VmpoY11e0Lt58+aVm8clVASmhEIkNo4aNYrWrVvzxBNPMG/evJDUWRlCyt+d0W1ctKVhyS2suWdNOIlySgrcoa1TEQpO1UW0I474qEYkRjclKbYVMU4rRL5TC19KhNrC76mjhTkLeVnUB4ElVN/K2rVrSUlJoVOnTrRr147vv/+ecePG8cYbb9CzZ0+WL1/Oli1bePfddzly5AhnnXVWiFqOLKr9fW7YsIFZs2bRo0ePUuUFBQVceeWV/PWvf61yXfPnz+ehhx7iqaeeYvPmzfTs2ZMrrriC9PTaSxR3uiNkzV/2JYPeAYGgdzfffHMgjwsQnjwuIrRxTN555x0OHjzI1KlTmTBhQkjqrIxQBR+sT8TFWIZ/+aGWBkJMtBOKPLUzfiz0WMKb01H/BZZApNs68C2uDwJLIFlaDbn33ntJTU0lJSWFlJQU+vXrx5tvvhko9zN79mxiY2O57LL6l76kKlTrLsvLy2PkyJHMnj37lEiZDzzwAI8//njAirkqPP/889x9992MHTuWbt26MXPmTGJiYvjPf/5Tne6FBSFDF4elrghF/2fOnMmzzz5L9+7dGTZsGLNmzaJt27aBPC49e/bkhhtuCHkel3AZPY8ePZqvvvqKjIyMsNRfCl8k098TCTHWlFB+UWRPCcW6BG5v7bx03R5LeHM5QmeUXlcYdRiHpV4Y3YrwmxK8+eabnHnmmXTq1In//ve/LFmypN4HCC2Pak10/fnPf+aaa65h8ODBTJ06tUYd8Hg8bNq0iYkTJwbKFEVh8ODBfPfdd2We43a7cbuLR2w5tZHfQESG0VF1ETI0uZDKC3oX7jwu/t9fTf8G5WX7bdSoUU27WCmWhqX+TisGi9RN4n1Gtzn5RRR4dBQhUBWBKgRKBLmjxkdpePXaeekWek8fDYtfaNBsG5YyUQQhdWv28/XXXwfWn3rqKZ566qmQtxGJBC2wfPjhh2zevLncLLDBcvz4cQzDCGQq9pOcnMyvv/5a5jnTpk1jypQpIWm/qkgJWceL2LFkt2+UXMZoubRDAcfSC9C9Js1axJbYL0ukd5CBclniPFlixdIoWvoF3ypgSe6Bdd8HAVlGNju0nxAxXiTSN8cs+aVwZz0PExgaL6Hysv0KIUIetrzgvSnIfevw5+kYrLiJyXaw741vEdIMlJdcipO2T871IUrt45Rj/OcXxw3y3UdIpG6w1+zBXq3Y8Ncs0pG6RHEq4Cj/BqnS9y59U14S0j2ZuIU3sOv971OYui7vlFN8zsAIQJWS6WteoUXeMTyaEykEUiiYQrHWFWsdITAVBXnSuhQKUhEgFKSvrOP+7QD8dkZPqxV/GuTAOiAEVxcWcZku+ez2f9P5Tw/T8aKrK7/eauL2WoOtVWtX8e2mbxFCEOs4TtsG6SS4EhE+V2kRcJMWlnu0fxsF4XefFr51VF+Z4nOlVgL7FKEWH4+w6haq77miBNrDv16iDet78p3v+0sF2kVQlLefpCjJpn07UVUVUIlyxtCrTauwCaRbj+awJT2HQ/luUASTNu8j3zTp7HKhnPRchGIPQ4XSz09/GYFyUfoZ6//4Cvz7irckIusETUyj+BntP9bXj3WpXqSIXHf++kZQAsvBgwe5//77+fLLL4mKCj7leaiYOHEiDz30UGA7JyeH1q1bh7XNIgMOnyjiqy0Hgj5316+ZYehR+RyLdbC0+2sI6Rd0FIQC5znDkwCtNghVaPuKsv2GOmy5Y9urOKI8eAotO47ODieGdMLxQz7PAb/Lo/Bp8KzHp/S/TAk8XYuPQwTODbx0/ef4totHdP5ta38j90Gk1NnrOCcg4CqqgtQNFFUBVa1wLFhqWq7s1YBA0Ca2Obpp0KldRw7ne+kalQyKhmFKTFNimJbBpiklhuFb6gZnfnKAgkbNyLrgMjANpGGAKa11KcEwwDSRpgmmtU+YJpgmSLPUumIWa7M0VfUld5PWubJkAjloKHV0JY8WPx5h/9IPwyqwtG/WnsK4QsgBPUcHCS3bbifeuR+PJ4HiBHcShCXYClFCKBXlLfGt1x7ntbA+nmIzCgqBH9zv0K9z/7C0efOc7/HmWMKwmeDg39nZ1o7c3LC0VxmuVakIb3maU1tYCSVBCSybNm0iPT2dPn2KR2iGYbBmzRpeffVV3G63T8quOo0bN0ZVVY4ePVqq/OjRozRrVrbbn8vlCmlQsiohBI1bxnLHPReWfE+UeFqXnnIRAgyviUTgcCnF+0tK6IHtEutKSfmdwKhVSpCmLLVdap+USBP+PW8JsfsS2DwmdB46kUBx4Lj6M6UipUK+4wJiJy8HoK4nADzPXktTw8uYx8bVcU/KxvQa7HwWWt04mnMeHhPSurtW8bivB/TA7QmvR1OrRq345yP/LFX2/pprOeF2cutVm6pdr2maSCkxTQMpDUzTwDD0wLqUBiAxTR1LQ2wipeH7TZmYpgGYvvKTypDIwHnWORITaRrkeiWqpiGlQWbuQdwZ0ylyn6j2dVSGXmTQ9awmTBjYEYcqKAAKDJOmDhXT9PW2WHntU2YX6wj9M0l+YdWf1FgiA+uU3JYltksc/9WBAyxA4+kB8bRJbOB7FktfG9ZBezLdnNG8tJ2nTfUJSmAZNGgQ27ZtK1U2duxYunTpwoQJE4IWVsBKjnbOOeewcuVKhg0bBlg/vJUrVzJ+/Pig6wsXAoHiVIhuVHeapaogogwUs/5H0DwFUa/nsyKEyLEZKZNA9+rQNkFVMAxv5ceFGGu6pmbX7c8IXp3ncKjYn76P3RnTw9qGNCStGkYzsHX47c4qwn3iIAvyoF/3jpzZvOzB9eBa7tPpTlACS3x8PGeffXapstjYWJKSkgLlaWlppKWlBeb+t23bRnx8PG3atAkYNg4aNIgbbrghIJA89NBDjB49mnPPPZfzzjuPF198kfz8fMaOrUoQp9qjPhjdCg0U4/QTWOpTaoTCpa9g7vqGKNWLtx54MkQMEfBdmUKAUftaPKE4UKg/2sPyCe/fsMBrgCFJjK77mKd+TY2IIOPxcFL3v84wRLqdOXNmKYNYfzCxuXPnMmbMGMAK8X78+PHAMbfeeivHjh3jySefJC0tjV69erF8+fJTDHHrEoGIiAdqZQiN01PD4qM+TAmp3/4Dl7MIU6qIlj3rujsnEcH3sDhlpdaRqrDsYGoZRWj1PJa2RfEVhOdveDjXco9PiqnrCVZr6gks8+jfC3U9cKyxwFLSvQpg8uTJTJ48ucJzyspzMH78+IiaAjoZKzR/zR8oy5YtY9KkSZimia7rPProo4wePZr09HRGjRrFnj17cLlcvP7669UKFa9qAmFqgYSEpwuB0Px13I+qUug6n9inv4io3BehCBEeViJgQCAVYRnt1jKK0FDqzd1dAWH+G6bmWx5WTWLrXmDxX2qonrNH96Xy8bMvYRpeSlrSlGiRpu27cOPjfwpJe9Wijp8hkfQ8Pe2RUnL77bfz9ddf06NHD1JSUujSpQvDhw8PBNtbvnw5GzZs4IYbbmDfvn04HMFZmSuaQKJgmhJVjdw3VG72IR77eDi5prfYMA4w/evCWve/Og6iWz8Wd914AgRPJL58RLW7VV4ma7fbzcMPP8wXX3xBVFQUPXv25N13361e7yJAwDYVAUbwX1JRURG33XYbO3bsIDo6mqZNm/LGG2/QsWPHKp2vKI7TQmDxD+q2Hs0lc9dRoh0KcQ6NWKdKnFMl3qkR71CJ0dRq/b3T/AJLTN0H3fMb2Cohum93b9xGXsZ2Yht1QlEdJRSOAimhIDuVg9u/BepQYKljbIElCEIRVr1kfqWcnBySkpJwuVwsWLAgYPfTt29fWrRowerVqwNpw6tcv+8v+umaVagOKwaDgsKerN2c1bYzA8++sMbXUBbSNPnq+xnkFlneAZZVfsAc32dx748wIzmUe4hvKKSnGsMZjgQUimPN+FWs/ugQAsF5eQW4f8ig83V1/1I7maLVCzB+WRMYfbiU6hltlqd9O//88wOBEnVd5+eff+bHH388JS1GZSjGCTCr5wFTXibrxx9/HCEEu3btQghBWlpateoHIkLDgqi+hmXcuHFcddVVCCF49dVXueuuu07RQJfH6SKwFPmGGF9sSeWXlRvLPU76PSIVgVAFQhEoqvVRVQVVFWiqgqYpaKrAoSo4NYW0XOt30Cy27gUW/11y6JedmIcOoygKiqogFBWhKNa2oiJUpXhbtbYVRUVRFISqgClQNBWv7zd+y5N/o1HzU5PGvv/kc6Tv/RHTNAMG1rVPPZ8S+r1gJT+s2QNFCMH8+fMZPnw4sbGxZGZmsnjxYnJzc/F6vaXcuNu1a8eBA8HHfElq2ICjwKH5/hva8H3aslE7zsBXa3QJ5ZJyYC33/xZcEsFGhsms6+YR2/jMSo91b/6avc/dS9T1kXfLimUPEhtdIiiaC9yN2gdVR0Xat/Xr1weOW7hwIVOmTAlaWAFwFP4U9Dl+ypqezM/PZ86cORw6dCgwWi4vFEF9QSoCUY0IqlFRUVx9dXHsln79+jFjxowqn68IB7GqxDB1VCU093h5ArCfVatWMWTIEJ577rlSCWxrgsNp3Qc3DO7I3XHnUuA1KPAaFHoNinSz1HqRblDkNXHrBm7dxFPi49VNdMP6uN0SwzAxTImhm8Q0jqJbUmxI+lsT3PmAAqvmvExsUX7I6nU4y9aqK4qC4T3BCyOuO2lPWUJEeYJFWQGUTj22rJhL5mUG7iK9nHprh8h7+kcoobBh0XWdqVOnsnjxYi6++GI2bNjAddddx9atW0PTSeDGgVeS3iUTt9eLKQ1MaWIKkw+WfkbCjg4ha+dkPLr1g32n92Oc3fVGX1RMAtEyheLToQhRTdW/z4YlAsNxCwzy1fOIfeJLwNI2xVZjBFSe9q0kc+bM4c4776xWPw1na4SeF7KAx3v27KFRo0Y888wzrFixgujoaCZPnsygQYOqVV/gb1uHgzhLYKm5DctLL73E9ddfX+XjE+POQM+BAyc20r5x1fOwlUdFAnB8fDzZ2dk8/vjjpYSsUODP3tylcRwXtwmP00RGRgYDzjs3sF1QUMDevXtJT09n+PDh7N+/n8TERMDKFfbggw+GpR+u+GTIP0G/mx6naZRixcExfXFsfOum6Qt0KCWmNEqUFx977EAOmkOQ0NhFfFJD4pMSy2xv0B0j2PJFK6QpS8R8KQ4cUxyfq4Q2OxDMq0QsGnlyPK/iWF7F9fiPKS4XYhbO6PAGaK0MW2CpKr55xJqwdetWjhw5Ehit9u3bl1atWvHTTz+haRppaWmBEWpKSkq1kgcKIUhufmp8gtZNW5C7I3xqROl7yDu1KByOmJDXH3AdjIRpg1OQoBR7ZolqCitlad+czmLjwoMHD7J69WrmzQtOk+VHd52JIDtkAex0XWf//v1069aN6dOns2XLFoYMGcLPP/9cPQ8/v6BQZ+ru0BjdPvPMM+zevZuVK1dW+ZwmCT1IPQLHc/eFRGCBigXg8ePHM2nSJBYvXhyStvyYAbuO8P0Nk5KSSg3yZsyYwerVqwNhM1544YVATK9w4n8SnXVRZ1o2DP0z72SatGnG5XffHPZ2yuO5//wHzRn+66wIW2CpIlbk2Zq9LFu3bk1qaiq//PILXbt2Zffu3ezZs4fOnTtz8803M3PmTCZPnsyGDRs4fPgwl1xySWg6jy/umhTsT90M0kRK3RfC3PCFKjeQpgG+CJf41q0y/yjBKD4eM1DmNTzs3v+11VBhmNIQBLIfRp5bsxAm1FCNX572bdu2bTRubM1nv/XWWwwdOjSwHXxHFUJpDNymTRsURWHkyJEA9O7dm/bt27Nt27ZqCSz+P21dxgiUilIcYKMazJgxg8WLF7NixQpiYqr+cDekZb/gUKOr3XZJKhKAFy5ciKIoXHfddSEXWPzJEGvT1XfOnDlMmzat1trz479LQmV0a1M5tsBSZWo+JZScnMybb77JLbfcgqJYKsRXX32VNm3a8M9//pM//vGPdOrUCafTybvvvhu0h1BFOPOPoRDP0C9Gh1XlHivCdUtFuIalhtddnvbNr7WQUjJ37lzeeOON6jcilJAKfI0bN2bQoEF88cUXXH311ezbt499+/bRtWtVA+GXRvoDttXhC0AqolQOomB4/vnn+eCDD1ixYgUNGjQI6lyPbsUX0dTQGJOWJwBv2LCBqVOnVtkYOFhMnylqbb3E161bR2ZmJkOHDg2UPf744zzxxBN069aNadOm0aFDeKbCA27NYandpixsgaWKiBq4hJZkxIgRjBgx4pTy5ORk/ve//9W8gXLo7mrAamBO57sRmsOatghkdLWyr6KUyOIqVGuaQ/gzuFrlgTLFysyKEDg1F9OffImvvlxL+wN3smVLn4Dba8iI4CkhISTUMBx6Rdo3sAwkdV1nyJAhNekp1b2Jy8tkPXPmTO68804mTJiAoijMmjWLli1bVqsN6bEM+kQ1v8vyXKyXL1/OpEmT8Hg8xMTEMGvWLHr2LDugX3WNbg8dOsTDDz9Mhw4duPTSSwEr51lJg+mK8BqW95YzRBqW8gTgTZs2kZqaGvh9Hj9+nKVLl3Ls2DH+8Y9/1LjdULv6VsacOXMYNWoUmma9yubNm0fr1q2RUvLaa68xdOhQduzYEZa2/XdJbQVTKy+0gJ+5c+dyxx13sGTJkjBNidX9s9cWWIKgPoTmLw/NZ2NxTu9xqGHItH3nqD/xj8n/OsXtNWT45wkicErIsmGp2U+pIu0bWA/msWPH1tCdUUA1w7+Xl8m6Q4cOfPXVVzXoUzHSa7mDi3K8JCqjLBfrzMxMRo4cyZo1azjrrLNYu3YtI0eOZPv27WXWEZ1dRH7j4D1QWrVqVSMvQq9haVicWmgElvIE4N69e5dKNDtmzBh69eoVMi8h0/f7FCEz7S6fvLw8FixYwIYNGwJlrVtbRqFCCMaPH88jjzxCRkYGSUlJYe9PuCkvtABYNo+zZ8+mX7/Q2D+VR11rk2yBJSjq+s9VRQzd92KXPgtwf/ZVkEZ43NKqE5U3KBSFY417kv1NHtqh/5ZyybMGc8In05RIpS1EcXZsxe+15M+MLUqVe3U4lAotmwmiYwRCKPgcnFAUy7NJKP6PNYIUitVWG01iFqVyYtM3JPY4D9VRPbPW8rRvAO+//3616iyFUCJSQ+VHenwCSzWmQstzsd64cSNJSUmcddZZAAwYMIADBw6wefPmUlnnAXJS99PiQD7HskLnolpVdNO6docamsFEZQJwuAiEq68FDcv8+fPp2bMnXbp0AaxpsIyMjID91KJFi0hOTg6bsOK/1tp6K5T3jDVNk7vuuotXXnmFhx9+uJZ6UzfYAksVERDRD3s/6d9/y6fvpKHLKEypYKIhUQFfivN6mvW4SMSw7exxUAj8Er52Dh8pGXO3aoxq0oh474/w32s48PMTtBn1SNj6VyNCbHQbaqTbL7AE/1gqz8X63HPPJSMjg3Xr1nHhhReydOlScnNzSUlJOUVgiU60jJmb5MDnd15D4s030uW8K2nUqEXNL64SDFNHAI4QxWCBigVgP2+99VbI2oNio9vamBGaM2cOd999d2Db7XZzzTXX4Ha7URSFxo0bs3Tp0rC1H7BhqeNH6vPPP0///v0555xz6rYjtYAtsFSVeqJcyT6SQaHZkH7nZuJwqSiqG0URKArENU1EC8JzIZIQ0Va/r70JWg+61BcnwPcxJZg+cz//tvSV+WMVlCgLxBswi+uQpkT3GJbCxbdtfUyfM5UvvoJhxSgwDd9+JAXu99ETFWI+ug7hyamrr6hyRPWnhGoD06dhoRoalopcrBcuXMjEiRPJy8vjggsuoFu3bgGbh5I4YmIxnp+E+tBU2n27F759lm8vXsi1by6r6aVVioGBBiiinicu9Wly1Vq4jnXr1pXajo2NZePG8qPrhpqAl1AdZmvevn07ixYtYs2aNXXWh9rEFliqSP2xX7F+PN1vGYQzoUHddiWEBILN+ZI6lhV8rq51R0ULIz3jroKISBsgH7o1XalUw4alIhfrwYMHBwxh3W43zZo1o1u3bmXWc/bVI/FcegMnjuzl6HU303HNPg7/uomWXcI7ejVNv3dNXd/FNcOfhikS8kKFG//AR63D0ezatWtJSUmhU6dOAKSlpTFu3DhSU1O59957Q9qWFHX/bKvfv45apx78CCM4XkloqPsfTXlIFEQEazCseyOCvz+vz75KC350XtLFGijlYp2amho47umnn+ayyy6rMCmhMzqGZmecjWvO8wBse2FK0P0JFul3B67nj2S/0W1txmGpK4qzNdddH+69915SU1NJSUkhJSWFfv368eabb4ZcWCmmbv+u9fvXYVM+tWxvc88999CqVSsOHTrEFVdcUeUstVUn8h+AUkb2lIv1c49ggcU3JaRUw4YFYObMmTz77LN0796dYcOGBVysn3zySbp06ULHjh3Zv38/c+bMqVJ9XfpdRUrHOChyV6s/weD3qqk/mtyykbUch6UuCRjd1tK1hv8ZG/nYU0KnGyWmTmqT8txef09IX8apSEVGuA1LQMNSzYCJ5blYz549u/p9UhQwjGqfX1X8L3gzgu+fqlAc6fb3MxaurVm8qjxjwxUQMEAd356/n7vq90IdCSy1RSRfVaRPCYlId2v227CUYRBbV4Qit1BV8Acfk/V8Krc4Ud7vQMPi+ynVoc1tLVP3F2oLLKcdp7fAEslEusAS8VNCAQ1L5Ags1DC3UJWb8Q3TzXr+uzWlpY1SlXru7VQFAlF9I+BFXjvU/b0ZQU+GyMZrSo6nFzB18jeApf6v0n160t9YIEvJEhVWccq5pXdFtYnjkbt6nXTQ6W50W3Muv/xy0tLSUBSF+Ph4Xn75Zbp27cptt93Gjh07iI6OpmnTprzxxhtBzRObpqAwJ/z2DtUmwqeE/F5C1YnDEi6EECTsTWfx6EvB6UA6HbS+bCjn3Rhao0b/z7beCyymBwBVhConeHiZd/g4qW4v0aqCQwim7j0CWBovX3xJa+n/+AJOCkC6TdDgX8+utzyFAvEqSwSsLBGkUpSozFr1HScELbo3YuTgM2r78oOmttIQlEfkPBkinMIohXxDRXG4KOXdVdYDphJ1qAzmb17OsUVHCtB2ZFVwQv1+8IWTBQsWBJLTLVmyhDFjxrB+/XrGjRvHVVddhRCCV199lbvuuiu4OWGhoIUuX2XIkbWoYUndeozc1DxfXioQaolowQLwRQ1WFIGiKQhF4EnJBiB7136KFBXh1FA0B6rTgeJ0oDg1FIeG6nQgXA5UNfwK4gZXXU3hp8tpcLwIxZNH3LF88ne/DSEWWBShYsWmjmCBsgpI6UEAaoiSOIaTI0UeHt116JTyWFXhjGgXVk56aYV5ktZfRkqJIf2/IpPOR9yoURpC+gymZbFCzooV5Zvm88ejLFHu347O9PLbiSKoBwJLXWMLLFUkWwO9hYu/Xt+3rrsCwAvv/IRn64lTygOyUj0fqZVLCC6rZCbd7OxshBBERUVx9dVXB8r79evHjBkzgqpXUQWJ2es4umKhZfdgGkjDRKgKSQOuRYuNC6o+fe8vGLu3AP4HnC/QHWbx31cC0vBt+5+k0tKwBY63lo6MFZiGk4z5O3378KVu8DUoS7bje7CagOk7JhCs7+Sy0udIU/LJrhyCNVVNyD7GuUD2lIeqdLxn6Fh6zngsyFaC48K7/gZ3/S2wveCuwTTbH/rggAEblno+0DBNDyqgKREsufvYV2hpQ8e1asw1TRqQbxgIBJc0jKthzq7geHb2Fly7IzjgZARhCyxVRCDJ1sPvLVBVylXS2FNCVWLUqFEBj5Jly06NZPrSSy9x/fXXB1WnW0misb6NhG/uPGXfkcI8WtxwdxlnlY98/w5cemgzzRaZl1K4JT34E0++4USJQlG63JQSAzjnrEZ0G9YBaWBFHTaLBRrM4mjBpiGRhsTQu2KkdkEoOtKrY3q9SF1Heg1MXUd6vZi6DrqBnD0d/UBKNb6BmqEo4TVcNmrBwDecSNNnOK1E/pTQEV8qiF4JMZzfILjBRKiRvxs7mJphCyxVJFpVaB8b+izH1cVTWEBUkcmchz4qHpNJMLwK0JClU+5DUXTfs1WCaaDrBlKoxbkB/T+TMrYtta5g4P89QWLXC2vz0srBH6UpNLW98847ALz99ttMmDChlNDyzDPPsHv3blauXBlUnYkPLyc3PQ2hqiiqCqr1XUe/0Y0WPz6CuXUSpZIzAsUXVLy0pm4EDrJwN7gG7fYXQCiBJI/WHEvJyXW1uAx/UsYSZUJYvpdCECMEMSK8XhzubDdM+Ja4FrEktE4I8uzmVTpq46dLEb7szrWKqobFa0gRAoPTQMOCX2CJ/FdLapF1/7Ry1bFwZUpkPXB/iYR7M/LvKpsyORGTg7uJg7NyS5hBCdAcOi65DU0zLTdW/y6h4dTcaNGx1gvRN+cqEda0QMCt0qfxN0x2HynkzK3fRIjA4ie0L9rRo0fzpz/9KZCCfsaMGSxevJgVK1YQE2TeJUdsHI72pxrppvV9DXfaPhL3ZgEQ1cWXiNI/XWNt+Bb+LNsmXkzU/n9AbZxcrWurK7xF1ktLc4TxKexwIL2e8NVfDoqiBu01lOMtJN/rwZQmpjTRpYGUEt03ladLg6OFuTQCMgtScWiJpe6K0tF9/FNHAtMoxDBycbiaI1AQQvWlrVB924qVVdxXh+JLaSHwabqwItNK36tIBrK7U7yNDBzrXy/uWXFvrIWk0JOJE9DqQU6ko75AhW2i61Zgkaa03OdtKsUWWOop7oaSpf2cvHDVTeUccX+N6vfmZPDy3aPxFubjPnYQ6xEpEZi+wb1v2zf1JJCgqNCoQ3hiVYcoDnZWVhYFBQW0aGFl4P34449JSkqiUaNGPP/883zwwQesWLGilJ1LTWl2ze0AHHp8LflNY+j8x9M7q6rh9rm2OkP/0vrxwWmoX3xIrOkhv/25Ia+/MoSqBjUltPn4fq7+KR1ExTYdZ8rWPAUc3zWe4zXsY12SCOioHCiCRiFQSN/7/dus/e11NK0RyQ168tIF99OlQdW0cJWR4XOjb1rHXmmmKSMhxEm9wBZY6imKCEzghAWhOhBIvvxiE19+salK56jC5IaRV9P22vFh6JH/JVGza87Ozubmm2+msLAQRVFo0qQJn376KYcPH+bhhx+mQ4cOgUR5LpeL9evX17DfxRQBZhhe4gDt2rXD5XIRHR0NwMSJE7n11lv57bffGD16NMePHycxMZG33nqLs846KyRtmqZp2fbqJtIwMQ1LM5efXgiA6gr9teoHD2A449GuvJGWQy4Kef2VoQgFEcSU0KHCbBAOhsSk0jkuFiEEqlB8brMCRSgoAhTZnsPyOZpEFWv1ArqUgKx+knbDcxSKdkN8P5Cmz8PI9HmlmEgMnw22X1fijx3i09v4hH9/X/C72gZMgEsmGT054WjJqcxirc+G7Hw+zXLwvKz8b+81DfblZrAr5wj78o5yKC+dtIJ0jhceI6foGHlF6ejuFBoknkt29kaOpB/i5k8+Iz7hHAQKilAtLZKioaD4vksVRSioioYuBXmOM4hNHIAiBKqwEhWqAlQh2JFfiIBqGdguX76cSZMm4fF4iImJYdasWfTs2TPoesDSsPyOos/VCFtgqSJSyjr3QS9JuHuixSZw2/g7yEs/ZOlSpM8wTFpu2VKKEuWAhC8Xf86XH/2XmKWfWWrkk5xbynAqIbvIugXjndZoJ+Dk4uuHfzBrijiI+z/ycwtqdF1t27blhx9+KHOfDKMxpdUAYVX9zp8/n169epUqu+eeexg3bhxjxoxh4cKFjBkzhg0bNlSr/p2f7uWrz1IwS/x9KsIVFwZPEacT4XTSaEBfYtu3CH39lSA0LagpIetlaHJ98zbc1CY0gmIks31fGvuyUjlelMnnh/exNzeNA3lHScu3BJHsouMUeDLwejOReibiJF8yqSSgORoS7WpMs8RudG54PVP7jOCCD/oBkJh4PgKBKQ1004P0C2bSEtRMaSAxkdJA9xxDVzZxQOtLwGG85LMFaOoM/hWYmZnJyJEjWbNmDWeddRZr165l5MiRbN++vVrfmTQlDdM9TH1irSW4CGHFeBSCZj2TuOu6ztWqN5ScyM/ErRTWdTdsgSU4IkhgEcKXGyY8lDdir4iCw7+SdSwdIaz5cyvmRhkfxTeqUwQ//HiUFslxtGmRUGw0Kig2MhXW6C+3wMGuw1CgNgrbNYcbfyCq2iI9PZ2NGzfyv//9D4Abb7yR8ePHs3v37molTss8nIeUcN75yYEYKkIBoSi+bV+8FUXBFavRvHfTUF8SasNGuHJSyX34bo42as8560718AoniqIighBs1UAE2/B7/5QVELFNmzYMGjQocExBQQF79+4lPT2dRo1C91vamZ3GzZ+NxNQzaSwlfz2olz5AiUNzNCLamUTTuHY0ijqXpjFNaBWXTLu4ZnSMT+bMhObEOMq2J9k2elvQfbp++WOkZO3k4MDqaT7KY8+ePSQlJQU0lQMGDODAgQNs3ryZPn36BF1f34tbs0k7jCpBGj47IVOiHSokbetxqAWBxeP1kFdYQKOEBoGy9MwM3v92EW8fn4kuLHufRrENw96XirAFlmCIHHmlVkKAlTVir4h+D70SdBsDqnjckd8Osuu530Ctx7esCK8WZ9SoUUgpOe+885g+fToHDx6kefPmaL7cPEII2rRpw4EDB6qd6VURcO7YutMUnP3c42RsGsahl2ej7KneiLYmCEVFBKFh8Qssei0ILGUFRPzxxx/ZunVr4JgZM2awevXqkAorAL/lpCG96QigW7s/0adhS9rHN6NTQnPOTGhGrKP2A8kV6gVoanCG81WhU6dOZGRksG7dOi688EKWLl1Kbm4uKSkp1RJYhvRuzpDep9rlTP3X90g9fPfN7sMpvLHmP/yW/wsHlb3oqoc+5kXcf9GfWbf7B946MguPWkSMjOPqhOFcdeYVXNitbu3v6vHTv35QnqairNFQ7969q1yvEMVzx78nIsG1riaE6721Zs0a2rRpg9frZdKkSYwePZqnn346pG3ICLAN1KJcJPfvyZF3k+C32o+LpGhaUAKLVos5gsoKiHgyc+bMYdq0aSFve2jrXjwR1Z6GMW2Yf8mfQ15/dSjS83Fq0SGvNzExkYULFzJx4kTy8vK44IIL6NatW2BgECqkbiK08HjaLV63jGd+mYxAoaPSjZsb/hEhBB9nzGf0uhEA9FUvYfJVE2mT3DIsfagOtsASBNV9WJelqShvNBRMX4IK8V8NTh6xN2nSJLwN/g6QYUqk16ZNGwAcDgcPPPAAZ555Jq1btyY1NRVd19E0DSklBw4cCBwbNNV46ZYnsC9btoxJkyZhmia6rvPoo48yevToKtdreevUvsAinE5Uverfw7HCY0Asv2XtBnqFq1sBKgqIuG7dOjIzMxk6dGhY2hZCw6lGTqwqt15ItKtxWOq+9NJLA8b5brebZs2a0a1bt5C2sd/1PenaT9zx1jw0xYEmNOujWB9VqNZSUQPrQ7pdyrln9ii3TtM0+feX7/HakefoRHdeH/4iTRsmBfb/KXcMK35cQ7smbejbObRTaaHAFliqTGhfNFUZDVVEuL2EyhqxlxURtvbwR/Ctwy7UECkIS+bf/Px8vF5v4J764IMP6N27N02bNqVPnz68++67jBkzhkWLFtGqVatqTwdVl5MFdiklt99+O19//TU9evQgJSWFLl26MHz4cOLj46tWqaYG5a0TKoTDgWZU/W8Y54j1LWsnkmpFARHnzJnDqFGjQq4J8GMYRUSFQaNRXbxGAY202LDUnZqaSvPm1jTO008/zWWXXRby39WOxmvRjXQMbzMMdHR0DHRMDEwMDGFgYmL6lm6tgNXfrOR55wwUxfKUUhWBqmgoioLb7ebJL59mm/oDfcUlvD7qOaJOmqprGJ/IzRddG9LrCCW2wFILlKepqCw8fEUEMn+GibJG7DY1wxACvKF/yR49epQbb7wRw7ACknXo0CHw4po1axZjxozhmWeeISEhgblz51a7nVDOagghyMrKAiAnJ4ekpCRcrqrbOSiaVjcaFocDNYhmE52JQDZJ0dZIPzc3l6OHU2nRpjUxMeF7uZ8cEDEvL48FCxZU20OsKphmEdERJLAYRiExjvAILE8++SRr165F13UuuOAC5syZE/I2Cl1eWja4hE+vfKZKx986Zww7tE3cuvrG8g9SYXyLR7lnyKgQ9bJ2sQWWIKiOW3NFmoqKRkOV9yV8+SfKG7Hb1AxDCEQYjOg6dOjAli1bytzXuXNnvvvuu5C1VR0vp7IE9vnz5zN8+HBiY2PJzMxk8eLFOJ1VjzhquRfXgQ2Lw4lWRrPugjyOHdzFicO7yTmyn8LUw3jTj1KYnsmMrHiSczLZkJVJXKHllv/FtcO44dnQ2ZJUFBARLC1Xz5496dKlS8jaPBlpuiNKYDHNQlJObOWmFX9HUzScqhOH4iBai+LR7jfRNi6p8krKYfbs2SHsadkYpocorepTbC8O+yc/7duBYUoMaWCaZomlyfacH/n4+AJeOzKDYflXkBxbvyJogy2wVJnqDi6roqk4eTRUFRRB2NyaKxqx1xXhzH1TW0hNILyRk0AzWKqjYSlLYF+6dClTp05l8eLFXHzxxWzYsIHrrruObdu20bhx5TYHhseLYQo0aaCfOIHicoEQAQ8s6Q/8Y5pgGIF16fvg+0jTLHVcIDGjNH1Td9Y+Iz8f774UPAcO0OjbragSPrvjStQTOURlFRCb7SGu0GrbASQBHhVyElTUBtEUNowjq9M5FCY3Jzq5KVGz3gj51GB5ARH9v5s5c+Zw993BJd8MGukmOogXbLhp0eg8jub8yq70b5DSC1JHSi/CzCdGjeJf51XdZiqcjP9uFrszf/MFvvMHE1Qw9cygBJbmSck0TypfCLmV6/h+4RrS8tMYumQonw//nMYx4bHxCRe2wFJlgtdnlKepqGw0VBXCGcSuohG7TQ1wqAi3XvlxkUo1JJayBPatW7dy5MgRLr74YgD69u1Lq1at2LJlC0OGDKmwvh3zVvLVN5Lmqfl0NTz8dmH/4K+jBkQBugqOQ8fwJMWR26odBU0ak5PcnLjmrUls2Z4mbc6kYePW5UZQXTvrDURcaG1aKgqICJbBbTjxmgZCuol1hN6NuLp8cfXzp5RleYoY8EFforTIySb99W+zEWo8mpaIFanYyu6kOpPp3yy0bsRf3vQln+/7nMfWPMYNS2/gi5u+IEaLnL9ZZdRIYJk+fToTJ07k/vvv58UXXwSgqKiIhx9+mA8//BC3280VV1zB66+/TnJy+ZLfmDFjePvtt0uVXXHFFSxfvrwm3Qs5wY7yy9NUVDYaqgrhNroNN1JK3pv5Gtk5ufj1V9IfAhd/IjYwTAO314vHFBjJGr/s7YzzhwRi4qKJjYsmLiGG2LhoVC3yk63hUlEK6iDDcKgIUl4pT2D3ey/98ssvdO3ald27d7Nnzx46d648QFbO4SyETKDtZd1Re44l3lGE9CWxE/4own6XfyvuOv4M10JRfFFEfUEJfdtCKIEIo8J/vCIC5ynRUTjatMXVsSOONpYg0j24r6IU0QUFKFU1Lq4nZHusKKixETQlVBYZ7jwAEsJk2xIshk/Qu6LTX5hRSxqfq9pfRVp+Gs9vep7z3zufT4d9StvEtrXSdk2ptsCyYcMGZs2aRY8epV2oHnzwQT777DM++ugjEhMTGT9+PMOHD+fbb7+tsL4rr7yylEFgMAZ4tYGUJoLgfOIr0lRUNBqqCvV9hqQwL5fdR4+jGV40f04TASUtiYUAIRRioqJp4HSRmpvH3hO/snfZrxXWHS0SURUHDs2BQ3PidLhwupy4XC6iolxERUcRHR1NTFwUMbGW0BMXH0Ncg1iczjCEk/dfj0tFCZNbc20QbByW8gT25ORk3nzzTW655RYURcE0TV599dUquVtbmW1VvL9s48xZT1T/YuoI0zCILipEPc0ElhOefADiIkjDUhY7c7MAKJLh+50HQ6ZP0Kvt723s2WOJc8Tx9+//zjM/PMOsIbNqtf3qUi2BJS8vj5EjRzJ79mymTp0aKM/OzmbOnDm8//77XHbZZQDMnTuXrl278v3339OvX79y63S5XDRr1qw63aklzAizoxChdduoZUyfS2rf3r24YvjNVTonL6eArOM55OcXUpBbSEFhEYX5hRQVusnKOcHuwz/TLLEtQip4vB68uhd3YQ5Gvo4hvZg+l8BANrmykAoKGioaquJAUyyhx+GwBJ8olwtXVBRRUS5iYqOJiY2mReumtO1UeXAlxaEEFdY90rBsRKr+G6hIYB8xYgQjRowIug8N2jSCA7BJ9Kf8p0nkUphfgCIlzvjacXOuLTLdlsASX80Xb0ZGRrkpBHbv3s19992H2+2mqKiIsWPH8thjj1WrnSNFlobFFSGaoBM+jU91v7eacHPnm3nzpzfZfHRzrbddXaolsPz5z3/mmmuuYfDgwaUElk2bNuH1ehk8eHCgrEuXLrRp04bvvvuuQoHl66+/pmnTpjRs2JDLLruMqVOnlmuA6na7cbvdge2cnJzqXEZwSIkiwhN1sDoIIep3TJKApqHqL8C4hBjiEir6Yd9UaR2maeIu8pKbnU9+TgH5uYXk5xdQmF9EYUEhhYVFFBUV4Xa78XjcPsHHg7ugEN30BgQfKSzj2YTsbLr/tI1jSmPLWFOaCL/RpvQZePrKXLqBkJKdFxA4Fp/hp5VhVxaXS3+5z/iz5H4g6Z7HaHr/H6v83YWCvDwv7jrWEHW9/TIO//wBB45Gjg1CMOT6XLldp5mGJaXAegZ7qd7fJSkpqdwUAuPGjePvf/871113HSdOnKBLly4MHTq0WoHaEnw5cXok1m1OHD9+gSXRWTdTVH2S+7Bs3zIW/7aY4Z2G10kfgiFogeXDDz9k8+bNZfrzp6Wl4XQ6SwVFA0hOTiYtLa3cOq+88kqGDx9O+/bt2bNnD3/961+56qqr+O6771DVU20Tpk2bxpQpU4Lteo2QyEBeEJua4w+xX9taK0VRiI5xER3jgubVz6ei6wb5OQVsmfQkjdPSKGrfEoQCioJUFJ+dhIJUVStRpaKg6+ByqDiiHZathKIgFNWypVD920pgH4rqWxc+ewsFoSpkzZ+NO2V/CL+VsjF0k4L0AvKOFpCfXsjeg3lhb7MqFOSbRImiuu5GtcjLtl7sroSEOu5JaDlQkAuAVEIzlV8yhUDJmD35+fk4nc5q50LK9lpu5XUlIJxMpm8qLaGO+vN/Pf+PZfuW8dS6p7iy/ZURb4AblMBy8OBB7r//fr788kuiokLnvnbbbbcF1rt3706PHj0444wz+Prrr0upCf1MnDiRhx56KLCdk5ND69atQ9afspDSjCiBxQwyymdRYRY/rHoMwyywRvP4RvDICtalb9201il9jKUckSd98E25lFHuQwJFOsTF9an75DTVRNNUEhvF48rPpDApib6fvl35SSEie9HbPuPQ8LDtg19Zvza1TG1Kg9i6dyws8giitPppvFyQnYMDiEk8vQSWhqrl/dYnseZJFU9OITB37lyuv/56Jk2axLFjx5g1a1a1zQdyvZaA0CBCbG1yPJYA1cBZN/1pm9iWmzrdxMLfFjLkoyEsuX4JTWNCn2U9VAT11Nu0aRPp6en06dMHTdPQNI3Vq1fz8ssvo2kaycnJeDyegDTs5+jRo0HdYB06dKBx48bs3r27zP0ul4uEhIRSn/ATWVNC1mu/6m/7Azv/hzd6JbprPW65DbfcgVv+ikfuxCN345F78coUvPIAXg6hcwSdNHSRjimOY4oTmCILSQ6SfKQoRFKIxI3Ei5QGUvpc8kyBNFWk4UAaLqQRhTSiAx+MaGIbHiWxQRpn9a7b7J81Jj0do4qxc0KGNC3NTJg4uOMEqgIXXNCMwde04/oxXRn5+Dnc88LFjHzu4rC1W1WKdAfRIQz3MXfuXCvx28cfA/DMM8/QuXNnFEUJlIWKQt/0dVyDxJDWW9fk+jQXDUPw4j05hcD06dOZNm0aBw4c4Oeff+Zvf/sbO3bsqFbdeV7LyLWRq+5tiBam/MC7v74PhOZ7qy5PXfgUj/V9jBxPDhPXTqyzflSFoIZLgwYNYtu2baXKxo4dS5cuXZgwYQKtW7fG4XCwcuVKbrzRCg+8c+dODhw4wAUXXFDldg4dOkRGRkYgV0MkEGkalmCRPo3M2Z3foXnbqv8twsUX/+vKmZ060bJtu5DWO3fuXO644w6WLFnCsGHDGDhwIPv37ycx0XpBjB49mgcffDBk7TlOnMDsU8tRgKW0ppDCREGhTqOGUfQZHdpkbqHCjYuoGE9I6kpJSWH27Nml7OsGDx7Mbbfdxh133BGSNkpSlJ1NAvBrZiaZSGKcDmKcLmKcTmKjnLg0R7nxWyKZ3IAgULOpjZNTCBw/fpwlS5bw4YcfAtZgtl+/fnz77bfVsmHJ9/WzLgUEP5PX/gVhFmCqSZwRX7dajZFdRvLcxufIdmfXaT8qIyiBJT4+nrPPPrtUWWxsLElJSYHyO++8k4ceeohGjRqRkJDAX/7yFy644IJSD4QuXbowbdo0brjhBvLy8pgyZQo33ngjzZo1Y8+ePTz22GN07NiRK664IgSXGCoiTcMSHG49xVp6j9RtRwKIgB1LqCjr5QPwwgsvMGzYsJC2BaB7vUTl5qKHeTryZKQ0kShIKcNiA1ToNkhsWvcP9PLQhRNXfM1tJUzT5K677uKVV17h4YcfDpSfd955Na67PPL37cEUgrGZJmRlnnqANNEMA9U0cDstNZLD60E1DVTTRJMmmmmgShOHKVExcUiJhsSBxAHFSwEOIXxLBYcirG1VwaEoOBUFTRE4hG/pW3coClkHU0hs1IgGjRrjVBUciopLU3GqKg5VRdOgyFGIy+lEIDmYexCouSBwcgqBhg0bEhsby6pVq7jssss4fvw469evL2USEAz53nwkDhxq3U5tek0DYRYwqMtDPHPuaGKqMQA5eXCWnp7OqFGj2LNnDy6Xi9dffz0QnLEyJn4zEUMaEW94G/K/2gsvvICiKNx4442lAseVZOfOnWRnW5Kcqqr89NNPvP3222RlZdGiRQsuv/xynn766QiLxSLDGl02aIKbEULBepCoSmSEzracX0KXV6e8l084ydq3D9U0cYZYS1QZ0jTx7M7mwMS15AGFivXxqgLT95GqAg6B0FSEQ0FxqmhOa6k4VVSngupU0ZwaqkvF4VRxRqk4ozQKdUlcg0j67RXjzi3AVJ1EN6i5W+rzzz9P//79Oeec2puWbJl5HF1VmJccRaFXp1D3UqgbFOo6RbpOkWlSJA2KTJOsvGwSNJVoRcErJW5p4pESj5R4pcQDeKXEC3gk6AjcAvJR8CLQUdADSwVTCgyhYJgKJgqGVDCliqmqYGB9/DTpZC2zJafuhJjsJcRmLy59cUoczhoKAienEFBVlQULFvDoo4+i6zper5cHHnggKI19SQr0QoTiosDtJspRWpuVlZeP6tBwaQ4cigirQ0CWx6/pia2WsFLW4Ozxxx+nX79+LF++nA0bNnDDDTewb98+HI6KY85sOrqJZfuW0S6hHX/o+oeg+1Kb1Fhg+frrr0ttR0VF8dprr/Haa6+Ve44sEYsiOjqaL774oqbdqAVMKyJmPUVTrAzRDkfNjeJCQ2gfBhW9fB5//HGeeOIJunXrxrRp0+jQoUNI2szatQuAxE6hTStfGV5MdjVyEtejEbJIhyID4TYQXhNhmDh1ifAaqPkS1fTgkOAwwYn1cVXw3RtS4pWQU/e2tWWSf+g4ADFJNZt62L59O4sWLWLNmjWh6FaVMXNzQFUZclbXWm23PKRpYhg6Ht3Aaxi4DQOvrpN1IgNT1UBz+Mp8+wwDj2Hwxs9byIhOYPSAZzAl6BLOblB5LKLKKCuFwODBg9m0aVON6wbw7P+F2PxC3hhlmSzoqoaharg8xV5nEoGuqhiqhqlqGJqGqTkwVQ3pW5e+8xKjo1A0B4rDgaI5UB3WuuZwoDqs+E2a04HmcKJqDpxOB7Iwn/Tj+0FA+uHNrFESrOCWmhOnFoVTc+JyRhEfk0ijhFOnisobnC1YsCBg99m3b19atGjB6tWrS4UZKYtnNzyLQPDvy/8diq84rEToYykCibA4LPWf0E0JVfTymTdvHq1bt0ZKyWuvvcbQoUOrbbB3Mvn79uEEGlYhpHwoEdJEbRnP4D+cXfnBJ6EbJm6vgbtQx12k4ynS8bh1vEUG3iKdQ4dyYUUqm34+zpbJ3yIcAlVTUB0KqkPF4VRw+jQyMbEaVwxqT3xc7cVEyU89AUBs0wY1qmft2rWkpKTQqZOlSUhLS2PcuHGkpqZy77331rSb5WLm5SMrGfHWJkJR0BQn2kldalmJIfmH60HqGn8685Iw9i70JBj5OKSDRiPH4fV68Xo86F4vhteDp7CA5m3bo3s9GB6vtdS9GB4PhteLqXsxvdbnRFERmq6Tn5eHMHSE7kXoOoqho+heVN+6aug4jFPzh3k0Ey6HNRmfsSbjszL7KiS82fcl+p11WanysgZnGRkZeL3eUs4t7dq148CBA5V+J/nefFyqq15kb7YFlioTWVNCwb7qpfSFv48kmStEU0JVefkIIRg/fjyPPPJIUFmxK6LowAFwuXAm1q7HhyIlVNNLSFMVNFUhNqrsl2aHPA8//XQcWWhg5ngQJkhDYkp8aeut0bSCIBdYWmQw8tbaM87NT7emkmOb1yzw17333ltKMBk4cCAPPPBAWGydSiLz85Gu+hn0riRF0o2rmkHi6hKv4SZacTL2uutqpT0pJW7TxOP1Uuj24Pa4yc/OJjvjGJckOFDNXLy6B49uBaj0GB68hoddqTt4r3AZOYWljWDDoRks0utPTCNbYKkykTclJKqjoajglIrCY/sDNa1atYohQ4bw3HPP8cADDwTfvr8bUlTcmSAo7+UzdOhQjh49Gki8uWjRIpKTk0MirAAYqal4TgqSWBuo0vQl9Qs98XFOJv59QIXHSCnZsz+bL6ZvJja2drUFBRl5QDRxrZqErY2pU6cyc+ZMjh07xvbt2xk/fjxbtmyhSZOatykLCpBRkWkfFAxF0kOCEhnB14KhwCyqVUFLCEGUqhKlqiT4Y5c1aQIdK55GXr1lGe/9tIy4qNIRkcsbnE2ZMgVN00hLSwtoWVJSUirMz/Xz8Z95at1TpBWkoYp6kDwWW2CpOtKMqCmhoDUsviBgFV1CReGxwcoV9fjjj3P11VcH2Xppjh3bjdNZgGmeqioNJW63m2uuuQa3242iKDRu3JilS5eGrH5x7BhmCF5iweB3T6eMCNC1hRCC4ycso8HGjWvXm6gwuwjVEDjiQttuSVu8SZMmMWnSpJDW70cUFSFPg7D8bukhWqnl+EMhoMh0EyUiXzOU77YiB8dFl9beVqQZXL9+PTNnzmTy5Mls2LCBw4cPc8kl5U/Z3fHFHRToBTgUBzOHzAzPhYQYW2CpMhIlgpIf+gWW7IwUvls7DFQPCJ/WxR+B1rcUAlSXJRwEEyG1ZHhsgPHjxzNp0iQWL15cwVmVk5+fCkDTpuFJX1fy5bNx48awtAHgyMyETmeGrf4yMXzeGnUcq+PYMStQWLNmtTvKLsz14Aydc1mtI4o8iOTwJN677777WLp0Kfv37+f/2Xvv+Diqc///faZuU7fcGwZjGwdsU02HYCCQhF5CCNhJiAn3ksQJSYB7HQiEAMmXkEBIwHC54PwIxRQTLj10QjVggylxw3KVZElW2zbtnN8fKwnbqGtXWoHer9d4d2d3Z86uvDOfec7zfJ7ly5czc+ZMIJPLYNs24XBmv5dffjlnn312n/aVEi7b/QY+Xr+ckBUiZEWwrTChlkUf4LLhjnCUS4mW/y7Df13xVwhDSaz7ovB3v/sd5513HpMnT8ayLO65555OK4ROm3wa93xyD570uPbNa3nopIcwtPz8u7WS36PLKxSiZ8bA/UL9tk8wC5uRjdMxzWEIpZFRKgKhBKCBEIiUwAoXUza8eyWcu9pjP/TQQ2iaxkknndRnwaJpmR+RbedHA7Le4KbThOIJZA88WDqbclu3bh0LFiwgHo8jhODGG29s63i+I60VdgPdOby+LjPvPXpEPwuWZIAtsmMaNxBorouK5sZl9YwzzuCXv/wlhx122Oeee+CBB9oETDbQ0VgTqubsV85v93khQVcCXWp8e9hJ/OyUq7O2777g4BLqZb+j4447jqqqKjRNo6CggJtvvplZs2Z1uL4vpIXHyHiMcSM6r2jc8eJsxIgRPPvss93ex6UHXsoFe1/AJS9fwrvV7/KPtf/g9D1P7+2Q+4UhwdJdlOSV7dv59YrH0ASIlhRcgUCIjERojcC03v/suZbXikyyomh5zaZ0GqkyfXqU4rOOPUohWzJUMvcz66X6rDPPY+buAHy/sob/Agp2P4PZ+7R/8OgNO9pjV1VVcc0113yuhL23tAqWIBi8J576Tz9Fl5LwbhO7/Z6OptxKSko49dRTufvuu5kzZw6rV69mzpw5rFq1qu2quJVWmSK0gRUsyXjmb3frz14h0EBqAqmD0gUYGsIQCENDMzV0S8M0dQxbw7R1LNvADunYtkE4bBAOmxR7TYwpsrAKwpiFEeyiGLr9+atDxwHbCD63frCgex6qIDeCpbsmYdngrm/+jVVbPsT10ri+i+M7uC2LEzi4vosfuDxc/wzrGyv6bVxd4QifiNG7CNeSJUvaGvsuXbqUefPm8f7773e4vi+khccBsZ5XAfaUsnAZk4om8W71u0wry49S+84YEizdRBDgSLi3vhiFQLbIjoyA2Pl+6wKZ5NKWa+LP3rNLIkk52xEi8y6t5dXaDlvRUC3P0/aa0aKWrWoYNg4AnuVk7bPuao/97rvvUllZ2XaFVltby2OPPUZNTQ2//e1ve7x9w8jMIec6hyWXNK5eA0BhF8lzndE65VZXV0dNTU2bX8Kee+5JcXExTz31FKedtovzZMtUkOhh88vOSKfTfOtb3+Ljjz8mHA4zfPhwbr31VvbYY48O3TO/edJknjLXIdIBnhPguwGBJ5Eti/IVKu1nKowChZTgK4UvwUOQ6Ma4hPTRpYeuPHR8dAKSWiljwtuz9tn7E6UUehCgCvp/SuL8889HKcWBBx7I9ddf3+cE4t1GT2G30V2X8z+26J9E86TRIICr+YT13gmW4h0S7BsbG9uinB2t7wuO5hHrpw7Oy7ctRxc6e5XlZyuOHRkSLN0gE4ZX/Hx4FfNn9/4qpnU7nz2WgNan3iGbNn/A6tUwbdjkXm9jV3a1x/76179OdXV12/Pz5s1j5syZva4S0vTMlbOUg7PjLkCyYj02UDy5d9/7jlNuhmEwatQolixZwllnncWyZctYtWoVFRUVn39jq2Dp/dDbZf78+ZxwwgkIIbjlllu44IILeOmllzp0zxwzMsYF353Rq335viSe9IgnXOIJj9WvvMuIW/9AcO73KRk+DD/l4iY9fMfDS2fEkOcG+K7C97fzleOz93+9P/GamtAUGP1cBv/KK68wfvx4PM9j4cKFzJ07lyeffLJf9u1qAeE8EiyeFhAxey8Ezj//fF588UWAnb7Djtb3BqUUrh5QYPVPcvYBIw5gbcNalq5ZyqmTT+2XffaWIcHSDWSLX4gu+vZ1ZZS32OFx33NiREs5mlTZO/nvao+dbQI/Ew0azILF2bwZQiHMXlZ87NqR9h//+AeXXnop1113HdOnT+ewww5re24nWqcds9jWIBQK7VT5NXv2bG644Qag9+6ZnWEYGsWFNsWFmVyC2lcbKWzeQMmphzJywug+fJL8Jl2zDQCzpH9zt1pLW03TZMGCBey5Z/8kiiul8HRJtA8CIZu0jacPkYu//e1vACxevJhLL720TZx0tL43pNwkUoMCu38icT/f/+c8vPZhfvf274YEyxeBVsFiaPlXDidomefP4gmsPXvsHbn77rv7tP3lK36IbQ9uwRJsrcTt5Yln1yk3gBkzZvD000+3PZ42bRrTp0//3HuFyEwtCpUdD5v2uOmmmzj55JP75J7ZE1LbaggQlI7q3xLx/iZdWweA1Y+CJZFI4Hle27TFfffd1+eE0O6SdpJIHSJ5EmFJu0mUgEgWplrmzp3LD3/4w8+ZUHa0vifUNWaEbWG4uM/j7A6WYXHoqEN5cfOL1CZrGRYZ1i/77Q1DgqUbeDKTYKjnYclXa5RGqcGTD2KamX4wEyceO8Aj6T1abQ1qWO9+2LtOuQFUVlYyatQoAO644w6i0WiHVUJK5E6wXHvttaxdu5bnn3+eVCqVk33sildbS3MohmXlj2V9LnDqagCwSnPTz+vCCy/kiSeeoKqqiuOPP56CggKeffZZTj/9dIIgQCnFpEmT2qIBuaY52eJK3E+5GK0s++QVfvzqAgKh0BBoqqUAAgF27wRLQ0MDyWSS0aMzEcBHH32UsrIyNE1j69atn1tf2oe/cUM8I2yLI/0jbCvjlfxr67+ImbG8FiswJFi6RSAzUxh5GWHRWqeEBodg8X0HIRQh+wJKSsYO9HB6jVnfgJwytesXtkN7U2633347f//731FKMW3aNJYuXdph8p4iu1NCrdxwww088sgjPPfcc0QiESKRSI/dM3tDULedeCT/vTH6iltfD4BdlpuTwqJFi9pdv3z58pzsrysak5nPG7P71yjvo00riNseJ+gHowuNQEkCFSBlgK4ZnLBfz0t3GxsbOfPMM0mlUmiaRnl5OY8//jhNTU3tru9L4m19i2Ap6ifBctFzF+FJjz8e9cd+2V9fGBIs3cBr6bWga/l3Bdg6JaTU4Cj1rK9fjxCKWCy7J73+xEunCcXj+ON6J7jam3K78sorufLKK7t8rxACJUSbc3G2uPHGG7nvvvt47rnndqp6OPPMM3vkntkbQomAiaGRbH/gJbSwiQjb6LEQWiSEFgujF0TQYxFE2Bpw/5m+4LcIltDwz3fg/SLSkMhUcxWG+zdnp9Ul9jdn3YxthbKyzQkTJvD222+3+1xH63tLY8v31hPTuN7iS591jes4cOSBHDku/xtZDgmWbuC3Rlj0PBQsLRGWwTIl9OnW1QA888wy7l9aQSB0lGaAbqLpmRbthmlhWRaWZWPbFuFQiEgkRCwcIhYJUxQLUxiNUFwQprQgimX273/jhnWfoilFZMLEft1vKxKBDLInUDdv3swll1zCpEmTOProowGwbZu33nqrx+6ZvWHUiMMI7zaK5HLIuBGlWpadUUqC9ED6mYUAYWdC78qrROgKdIXQRcYHxtIQpo6wdTTbRAsbiJCJFgmhF4SJHjIdrR//77gNDRhAqJdTiYON1hNvUbR/BUvSTSAkWRMr/U1jqgGAkoLc/z/Z1LwJgMnFg6PybkiwdAMvaBEsediDQhMtEZZB4mnS4GYO2uaYwyjxdBzHxXOdtlbuvpPCTzbhSB9N+egqwBSdT38EShAIHSkMlKaDYXPe2afzlUljcvIZGte2eLBM7r0HS19QQiMIsjclNHbs2DYH3V3pqXtmb0gVjGRrscaRZ05CJtLIeIogkUamXFTKQaZcZNpDOT4q7SM9H+VJlCtRfmuugIfyNHA1pNJB6CBMhGaCZiJ2utjwAI/0J89Q/sOv5/Sz7UjQ2Aiahm7m33EkFzQlGwAojuYmZ6cjkl4SU+afK3l3aWoVLIW5S0KXUnLXR3dx+we3AzBnQu+r/vqTIcHSDfwWwaLr+XegaXWNHSwRlpc++YhDR9r85JzT0brp1up6Pg2JFPXNSRqbUzQlUzQnUiRSKZIph2Q6jeM4OI5LorGOcHIbjz7zEhtn7E1RLExJYYzSwkjWojGJTz/FAkr6qTx0V6SmkVq9mmf/92E0IdA0DU2QudUFpmGyzwmHE47lRzlpV0Ql1BSFsSeOytk+ZNolaE4SNCUJ6hqpf6QWp8Kn9q5n0EImImyhRWz0iI2IhNBjYbRoCC0azkRhLANh6H2akpLxOJiDoytuNmhKZZJuSwr7N6KUESyD93tudprQA0E4lJvf7zPrn+FXr/+KlJ8ipIe49IBL2X/k/jnZV7YZEizdwJeZHBZD5N+UEC1jGixJt6MLmtmeLum2WAGwTIPhxQUML+46ee/TrTXcteivUL2Gt59d87nnPaXjCwPZMg0ldAvdtDAsC8u2CYVCREIhotEwBZEIRQURSmJRSouiDCsqIBa2cDZtRobDWLHcWKx3RXNBKdOXvwTLX+rwNa+v/g+O+e8f9duYeov0JYVKYBTk9mJAC1loIQuzvBg5qpS6+9ag2aWkV7W+QgHplqWx3W0oGbRMSQWZiKYKMlNTKmizFVAtzUbbEJ/dlqpDCA46mJW/Wsrknx9LqGhg/v/0F83pJgCK+jnC4kgHUw1ewdLoNGIFuRl/ZbySX7zyC0zd5OKZF/ODvX/QJ+PS/mZIsHSD1giLqfeuaVYu0VrN7AaJYCmx6/GD3IU6J40u57/+67+oaWimvjlJfXOcpniSpniKRDJFMpUi3RKR8VyHwHMJnARBsgFXeqSVT1wFdHQhHSjBQZ98wu6pFM8ceSJ+OEoQiaKiMUQ0xvhTv87M4w/P2ecDOPz5x/Eam/ADiVSKoO1WEUjFxjNOx2tqzukYskVTQyZXJVTUf78tLRJi/B9PQvoBMp5ExlPI5mRmGirhIJNpZNJFph2U46ECBQEoKcFvcawOQEkBgYlSZksjsEyzL6VUS+MvtfN9J0BQSolXQGJLzRdOsNS//08CJ4lm2mhmiOb69eiBoLnxUwwzgmmEMM0ohhFC5LCbczpwsOTgPbX9n/sq5Ei/L3hxAQrF3cffzd7le+dmJzlk8P5V+5HWpNt8rBLStExiWWvpdb5jUIsjJuZ0H2HbZPyIUsaP6N2VnZSSpniK2qY425sSNDQnaIyniCeSJJIptoRKCUatRbgeIh7HSMQxaqsYt20DvPQP+PcnWf5EO6OHw+jhjvuhrNMtAmdw/H9oqElhAJHi/r8Y0AwdrbgAuhG5yxbrHn0N3pSESr9YZdwNK1+kZOkZO60bU1hAUFbCUU+e9bnXG0phKpXJZ1NgtNQ7mojMfSEw0TCEhomGKTQMoWMKDVPoGJqBKXRMzcjc1wxMzcTQTCr89Vhq8J7aRslSKrXs98uSUvLJ9k/4yrCvDEqxAkOCpVv4LcZxRi8iLD/+8Y957LHH2LBhA8uXL29rIPjkk0+ycOFCpJT4vs8vfvEL5s6d2+PtG0bmKk0GvTP5Ukq1lEQHSOnj+j6O7+L5Hq7vMaxwBKEsZtuH9VpS2uysbQ86/o7XrFnD3Llzqa2tpaioiLvvvrtd99hd0TSN4sIoxYU9m0P+ZOo0msoH3lreNwykOzhchJu2JykFisryww011/jxNDYWoZIvlmBxazcAsPWAq7HKxqO8NEc5CcZoNRgRGy9w8IJMB2cvcPGkix94eNLFkx5+4ONJD0/6eMrHl37L/QBf+XgywFOShPTwcPB8ia8kHhIPhacUPuChSGiCGfWD9/sd5hcQCrI/TfNO1TsoFIeMPiTr2+4vhgRLN2hzuu1FldAZZ5zBL3/5Sw477LC2dUopvvOd7/DSSy+xzz77UFFRwdSpUznttNMo6GFvGl3PCJaVa+/nvdWPtqyVCBSW7mAIDycIoQmJEAG6kGhCohGgCYmudV5tsgrY7ByDpkXR9SimEcMwY9hmASErs0RDRURDhcTChRRGighbkXaTE6V0iZlNJLURPfqMXdHedwwZ58/58+czb948HnroIebNm7eTHX62abaj1Bx8TM62310C3US67kAPo1skGxxKgeKy3nXQHWwECRdfCXQ7/6K1fUG2JNgWzfga0bGfdXGeMABjeeRHJ6HrgycvY1eSMkVYy35J9h/e/QMAZ+35+YjXYGFIsHQDz2+ZZzd6flA94oj2uzsLIWhoaACgqamJsrIybLvnEZxPa5u579+nMrG4gYnDSkC0mFALDagnKYYhDB3QM40ShZ4JurbeFzpCGJkFHU3XMTQTQzeo3PYCerAZnQoMmcKUKSyZJhSk0RyF4jPHjNodxhRIDScI4cgwvgzhE0ESRmAwOgymObK9j9Jr2vuOt23bxjvvvNNWknv66adz8cUXs3btWvbYI/vlyG7aocBJ0FA+8B4b0jDAGxyCJd3k4qEYWfIlESwpD18N3pNpR6gWwWIU5N7srCt8X2KH8q+is7uklEOpkb2O3r70Of+p8/l4+8ccNuYwRkSze8HYnwwJlm6QDjJVQnaWmngJIXjggQc47bTTiEaj1NfX88gjj2BZPf+Rrd66nuc2Hs0vJiX4znHZVs5ntLs2CCRxJ05TspF4qpFkupmE00jaacbxmnG9OJ7fjO8nkEECJROgEvh+go/qplAwIvf+JZs2bWLUqFFtHY+FEIwfP56NGzfmRLDUbq4GIDZy4F1MpW6ANzimhPy4S5MAYxBfEfcElQ4IxOD42/SEINmAVGDF+tckrj1cL2DTxjTDb16AphkIPXMhpuk6QjfQ2pbM400fbyZQI9AMC6HpaJqGaFk0XUfTdIQmMq9vqagZNWsvCsYMxwyHsMIhDMtG07NT2ZMWLlEjeyXNpz12Gusb13PU2KO46eibsrbdgWBIsHQDpzXComdHsPi+zzXXXMMjjzzCEUccwbJlyzjppJNYuXIlw3rognnKgXNY8MgTuP3ozK/rGkWRQooihcC4br/vzlc/5cYnPmHxdwc+zyPb1G2qwgAKRw/81YvUDfAHR9WYTHgk+qEC9a677uJ73/seS5cu5ZRTTmlb/8ILL3Dsscfyhz/8gQULFuR8HGs3v0NFzQc8fvZtCCF2PjG2LQa6kVkyzs8mWstjXTfQWx4bpoluWOimkSnNN00My8JoKdM37BBWKIRu2Vh2CNO2MUMhzFAIKxTGDIUxTLPtJNwXVLoJVxqEsnTS7gsjRpSwPVHPW2/+G6kESgmkAklnn/PfRK3iTE4fsiW3T2YW1E63EgkvfH4LAg1DM9F1A0Oz0HUTQzfRDRPDsFoWE8OyM3+j1r+TZWPaNoZtY4RskppLhazitfdeJGSFCdkhbDNEyA7vtLRejG3aWsH7a99F3+YTG1lCpKiAkB0hbIf5y9pFrG9cz0m7n8RvD/ttbr7wfmRIsHQDpyXCEjKzU4a4YsUKtm7d2jaVccABBzB27FiWL1/Oscf2vINx2Ejh+vnfS2h9XQKAaaNznxA3btw4Kisr8X0fwzBQSrFx48asN+5rpXFrFWVA6bjsTnf1Cl1HZdG6P5doqYC0kdvoSkVFBXfccQezZ++c7N3Y2Mhll13GiSeemNP974ijkhSGh1E2bSJeOo3nuviug+9mnJ6l7yN9H99xkEGAlBIlM52WM2XSkPGMyQFCZGxjhIbQREZQiRZBtYOwMjTFSLOGmWODTFRB04k0rsEnP/Jyjv3tvXR0FFW+j/QdpOe23aqbZyGm/pTwdxZ2a/vbP9lEQ8Vm/HQaz3Hw0g6+k/kbei23vtdy67ufuXi7Lk4yQRB4+IFHIFtvfQLlEbRYU1hHCCqsKn648sedjsNQOpYySWrpz1au+/zrdo9N+kKIFRgSLN3CaWl+aGcpTNd6Mv3kk0+YNm0aa9euZd26dUyZMqXrN7eDqQU4fva792abLfWZSNXwgtz3+Bg+fDj77rsv99xzD/PmzePhhx9m7NixOZkOAkhU11AGDBs78IKlQGoMa3B59prXCHRBYGpIQ4CpIQwdzdLQTA3d0tEsHdPWMWwD09axQkbbEgobhMImyhAUhS2sHAgL0w1w7NwJFiklF1xwAX/+85+55JJLdnru4osvZuHChTzyyCM52/+ueJ7LsGHjOeXSrhtddoWSkiAI8F0Hz0njpVtunTR+Oo3rOPhOuuUk2nJCbRVHnpdZ53kEnpfxI/J9At8j8Hxk4BP4fkY0BX7GsybIdDxuqm+kCYv9C/+NqQUZ/xkkdUUzyXdnGdEaqWpxkVVSgh7g2N0feem0cZRO635kubtIL8BPO3w33kQSB8dJkXbTpJ0Ujpcm7aVxPIe0l8b106R9B8d3cII0+4yeye6le5DGwVEOqXSS37/9ez4x1nHPifdkfawDxZBg6QZOi3Fc2Oy5YLnwwgt54oknqKqq4vjjj6egoIC1a9dy++23c9ZZZ6FpGlJKbrnlll5f/ZtagDsIBEt1k4OpZ7/bbkff8aJFi5g3bx7XXnsthYWF3HXXXVnfdyvOtloSZhgrMvDJo2W6geFK4kDElZipAFOCIRWmAkuBCWh0/bdovXbbhMIBXBSuAF8T+AJ8XRBogkAXKF2gDA0MgTAzjQc1S28RRxq6ZWDY+k7CKOJIkoW5uzK/8cYbOfTQQ9lvv/12Wv/QQw+haRonnXRSPwuWFHY4S7lwmoahaRimSSjaf1Lhg7uu4p9Pv83IKz9E7GB5MPDptr0gnUIIBdbAl9Vrpo5lRigtiJANb2D1tuKr+qHEwv3nM5RrhgRLN2idErL1np+MFi1a1O76c845h3POOadP42rF0GWfBcvEiROxbZtwiyHZ5Zdfztlnn932fEc5AD1he8IhYmX/v1xH3/GUKVN44403sr6/9vDr6khE8sP7wTINsARHLzy0w9copcBXeI5POuWTTnk4KR837eE6AV7ax2u5/XBDA4WGxvCIhXQDlKdQvkR4EnyJHkhMX6G7Ei0VYOwijizA6kAcFSAQMvsiFuDDDz/k4Ycf5pVXXtlpfVVVFddccw0vvfRSTvbbGa6XxgoPjh5PHeHEm7H1YCex0hvS6TTf+ta3+PjjjwmHwwwfPpxbb72VPfbYg2XLlrFgwQLi8ThCCG688Ua++tWvZukTfIZKNiMAkaO+PQNJoIKsJu/mA0OCpRu4gYOB6lPjs1xiahI3C917H3jggTbTtR3pKAegpzSnfcpi+dfeIBtYlXWUxut59/BvghAgBEpoIDTQROZWCNBa1u2wXmgt9zUtU46utSxCgJGpckA3QM804EPT29YLw0ApgS0FxaURMAz8bdUYIzqfmhJCgCmwTAsrZtGZ1MqGzV/gS9Jpj3TKJ5XycZIebtIltmQd40fm5grw1VdfpaKigsmTJwMZoTJ//nyuvPJKKisr2/6v19bW8thjj1FTU8Nvf5vbuX4vcLAjA3813xfSiTghIzt5NPPnz+eEE05ACMEtt9zCBRdcwIsvvsipp57K3XffzZw5c1i9ejVz5sxh1apVbRdU2UKm4miAsL9YJ3aA0dYoKpIbBnoYWWVIsHSDdOBi5qlYATB1iRvkJhGvsxyAnpL2JcNig9cfoTO2lB1AKCEoGR7KzOlLiVASpGzpJyMzDfKkarnNRCeQmcqDzPM73s88J1SAkgGiZVFKtt0XrfdVQKAC6pVsa8JnztivixH3L7qhEY3ZRHcQrF5timrAypEt/0UXXcRFF13U9vioo45iwYIFnHLKKfznf/5n2/p58+Yxc+bMnFcJySDAkw52dHCfHJ1kkmwESkOh0E4Jz7Nnz+aGG26grq6Ompoa5syZA8Cee+5JcXExTz31FKeddlrfd7wDKtnScyuU79k3PaOhoY5tfi1hvlgXiEOCpRt4gUuOCxn6hKlJXL/vguX8889HKcWBBx7I9ddfT3l5eYc5AD0lCCSBVIwozH3C7UDQWLAnH3xjX352yYH9vu//u+INUkmPs244AikluB5Y+VGx0Rl+bSYJWy/5Yh1UO8JpigNgD1CX72zhpNKErOyXL990002cfPLJDBs2jFGjRrFkyRLOOussli1bxqpVq6ioqMj6PkllKhdFOHd/E8dxuOSSS3jmmWcIhULMmDGDe+7JTSKslJLFTy3itur/RQnFddOuzsl+BoohwdINXOlhdCNBcaAwdYXXxyrWV155hfHjx+N5HgsXLmTu3Ln8/ve/bzcHoDdsqE8CMK50cIfD28N1AiK+wh42MGIsmfCIRjMCRdM0CA0OARBszwgWo7R/vreOclbuvvvuftl/qj5zNR8qGOQRFsfFtrN76rj22mtZu3Ytzz//PAD/+Mc/uPTSS7nuuuuYPn06hx12WJvvSDZR6YxgIYc5LJdddhlCCFavXo0Qgqqqqpzt6w9Lf8vf4ks4Jfw1Lj72p4wY9sXyvBoSLN0gkB66lr+CxdIVbtC38bVWKJmmyYIFC9hzzz07zAGorKzcKdTeHVZVZg7Wk4YN7oN1e6xd34BAMHrswGTjJ52A4aMGnxD06zPVd8awwTf23uA0ZH4DdtHgrtpIOx6lxdn7m91www088sgjPPfcc0Ra8ntmzJjB008/3faaadOmdatxaY9JZ6JeIpKbv0kikeDOO+9k8+bNbTmQI0fmzvrgiaZnmaFN4zfn/L+c7WMgyeOJjvzBlT56HuewWDp9irAkEom2vkYA9913H7NmzeKiiy6isrKSiooKKioqmD17NrfffnuPxQrA2m2ZA8OeIwb3wbo91lc0ALD7bsX9vm8pJWlfES0efFNtQWNGsHxZpoTSjS0RlkEuWFxXEgpn5//bjTfeyH333cc///lPiouL29ZXVla23b/jjjuIRqM5qRLCaZkSiuRmSmjdunWUlpZy7bXXsv/++3P44Ye3RZFywUz7K2zza/EGSfPTnjIUYekGvvQx8lmwGIqGVO+1Z3V1NaeffjpBkHHUnDRpEn/729+yOELYuD0JKCYUZ1xn87XiqjdUbonjo9h9XO7Lmv/yw4wneNQQaAICBRKIDdB0VF8Iml0QoH1J+gilmzOiPVScH+XvvcXxFHYW/IY2b97MJZdcwqRJkzj66KMBsG2bt956i9tvv52///3vKKWYNm0aS5cuzckxQ7VGWKK5EZG+77Nhwwb22msvrr/++jY3848++ogRI7LfxuM7M87nu+/M582Vr3L4fgPfOT7b9EmwXH/99Vx++eX85Cc/4U9/+hOQqa2/5JJLuP/++3Ech+OPP56//vWvnf5xlFJceeWV3HHHHTQ0NHDooYdy6623tk1FDDQZwZK/B1VDE/SlSGjSpEksX768y9f1xbfCrfqE4fiU/XEsrjJIiDApwqS1MI4WwTWi+HoU34yizBjSiiLsQkQohh4qxAgXYEYKsaPFhKKFhKPFRAqLCEcKs9ILpS80bEvhWgLTzH0flZCpkfYkEycVEUiFChSGIdjtyLG93mZnfhit5KLnjkx45HU2e5ZpTboNFWf35NiRh9LTTz/NwoULcV2XSCTCokWLmDFjRp/3lw407CwY1Y0dOzZTQdcOV155JVde2Xc34C5xkyhlIHrReLY7jB8/Hk3TOPfccwGYNWsWu+22GytXrsyJYCkLZXrRJdx41redD/RasCxbtoxFixaxzz777LT+pz/9KU888QQPPvggRUVFXHzxxZx22mm89tprHW7r97//PTfffDOLFy9mt91241e/+hXHH388H3/8MaHQwF85+tJHz2PBknHLzVF/kSwRkgnKdY93Zl2LTMdRTjM4zQg3jubF0b0EVtBMgVuFHSSxZYoIKaIqiS46/mxSCeIiRIowKS2Co0Xw9AieEcU3YkgzirJiYMcQdgF6uAAjXIQVKcSKFBKJlRAuKCJaUIwZLsz4nfQQt8FBRfsnWBkL6aQ9yWE/noFhZE8gteeH0SpQc9VzR6Z8NDN/f1fZxonHEQjMWPbdkHf1UKqvr+fcc8/llVdeYfr06bz66quce+65fPjhh33aT+A6+FLDjg7uKFEbTgJFCC1HOYrDhg3jmGOO4ZlnnuHEE09k/fr1rF+/nmnTpuVkf3e+tYiYjHDIjKNysv2BpldH2Xg8zrnnnssdd9zBNddc07a+sbGRO++8k3vvvbdtvvGuu+5i2rRpvPnmm+0ajyml+NOf/sTChQs5+eSTAfjb3/7GiBEjePTRR/nWt77VmyFmFS/PIyyaEKhcNUTLErWJgFERyf4n/2fXL94BJSXpdIJEcwPpeCOpeCNuohE31YSfaiJINSOdZkh/Jn40L4Hpx4m4m7GDJCGVJKxSRFUKW3id7i+NRVKESYuM+HH1zOIZUQIzijRjSDOGsjICyIwUEo2Pwyjuny61E/cspvbdGlK1aQpGZieBuSM/jFZy1XNHORKtIP/Lr7OFk0xg6qFMJVeOWbduHWVlZW2JqocffjgbN27kvffeY9999+31dp2GGgDsgi+IYHETKJHbi+LbbruN73//+1x66aVomsaiRYsYM2ZM1vcTBAFvpd7l6OhhFEaKsr79fKBXguU///M/+frXv86cOXN2Eizvvvsunue1Gf4ATJ06lfHjx/PGG2+0K1jWr19PVVXVTu8pKirioIMO4o033mhXsDiOg+M4bY+bmpp68zG6ja8CDG3g26Z3hELH8fM7J6Q6bbBPcc8zg4WmEYoUEIoUwIi+NRxTSpF2HOJNDaTijSTjDbiJBpxEE16LAJLpZlRL5Ef3Ehh+AtNPYKfrsBKbCMkkYZXMRH9aOu28FSyFuoy4yuX0lJvwWPVBHQChSO4iOq1+GJDbnjvKl+iRL5FgSSSx9NwkGO/qoTR58mTq6up4/fXXOeSQQ3jsscdobm6moqKib4Jly8cA2O72bA19YPGSKJHb/l+TJk3ixRdfzOk+ALZs2UCVUcvRu+UgOTlP6PFR7/777+e9995j2bJln3uuqqoKy7J2yvYGGDFiRIe1563rd53P6+w91113HVdddVVPh95rfBnkdYSlNCKJu/ntIFvthxkR66NZTB8RQhAKhQiFRsLwvpcWKhmQSjRR8qunCVl+znNpvIRHs5dxsnUTPmZh9k9+O/ph5LLnjpQZJ1+tML//32YTN5XANLN/Nd+eh9KTTz7JQw89xOWXX048Hufggw9mr7326rOXidO4DQC79Avi7+EmUdrApx1kA9PI/JY89cWsEIIeljVv2rSJn/zkJ/z9738f0NySyy+/nMbGxrZl06ZNOd1fvkdYwqbAl/krqHzPp1bGGFGUfb+NH//4x0ycOBEhBCtWrOhyfTYRmo6zpZr69Aj2np37EGx0eITTLtobgGdveT/r22/1w3jqqaeIRCK8++67bT13Jk6cyEMPPcTVV1/Nf//3f/d5X7KhpaQ5R7b8+YiTTmLnoCvwrh5Kr776KgBHH300L7/8Mu+++y5/+MMf2Lp1K3vttVef9pV2M1PPoYn7923Q+YKfRGkD32E9G4wcPhpLmlQ2VHb94kFKj85y7777Ltu2bWPffffFMAwMw+Dll1/m5ptvxjAMRowYgeu6O3l6QKZstiOznNb11dXV3X6PbdsUFhbutOQSX+a3YLEMHV/m7/hqa6pRaIwozf7f6YwzzuBf//oXEyZM6Nb6bLPq6bewtQS7fW1O1y/OAiW7Zb7DpONndbvt+WF8/etfp7q6us2H54wzzuCKK67ISoPAbX/JCC6z/ItxsugOTjqJbWdXsHTkoQQ7e5n85je/4atf/epOlV+9wWnKTAXZpbkzP+tX/CToX4z/gw3JelzNIxJ8cS8CeiRYjjnmGFauXMmKFSvalv33359zzz237b5pmjsZ46xatYqNGzdy8MEHt7vN3XbbjZEjR+70nqamJt56660O39Pf+EpiiPwVBLZh40utwxLBgaa6eisAw4eVZ33bRxxxBGPHfr6kt6P12UQFPqvXhtl9bC1GFx1477rrLoQQPProowC8/fbbzJ49m1mzZjFt2jR+//vfd2ufRktVTUPcJ1Gd6NP4W2n1w2hoaODoo49m5syZHHTQQVnZdkfItI+wNCL7f0FOfN3AcZNY4ew6PVdXV3P00Uezzz77sPfee/Pyyy+3eShdccUVTJ06lT322IMNGzZw55139nl/TnMDoLCKs1+SOxCIIIXSB7/T8tLXH+CUh05GU4I9d8uBI3Ce0KMJzYKCAr7yla/stC4ajVJWVta2/vvf/z4/+9nPKC0tpbCwkB/96EccfPDBOyXcTp06leuuu45TTz0VIQQLFizgmmuuYfLkyW1lzaNHj+aUU07p+yfMAr6UGFr+euxZhsCXRt6asVXX1gIwYuSoAR5Jdql69SWa/WHseVTnUZyKigruuOOOnX4D8+fP5+qrr+akk05i+/btTJ06lW984xtdhuyNsMkRR4/llRc388ZdHzPnsgP6/Dk688PYkaz23AkU1qQitC+wD8v2NRt57s9/AUDXdeqaNzNm0tSs7qMzD6U77rgjq/sCcOJN2HqAMPPjKj751FMkV9SA0EFpuE1FKGUihAsECJFZEDvelwhNITQJqa9hFdQM6p7GL7z1FFesuYYjOYhL5/w348buNtBDyhlZPwv/8Y9/RNM0Tj/99J2M43Zk1apVNDY2tj3+5S9/SSKRYP78+TQ0NHDYYYfx9NNP54UHC0CgVF5HWHRN4qv8dZCt3t6ISZjSL1gjrlWvriFmjmD0QUd1+BopJRdccAF//vOfueSSS9rWCyHaQvmJRALLsigtLe3WfqeePIm1K2tZVdHMuMfXM+Ub+X+Akr7kk+vepijxWVl5ek0DH93wLsISYOpolo5mZxY9rKOHDPSwgRE2MaMGRsTEjBmYMQujH0z6+krF6++wqfojxgybgu85jCjejd0Py4+ocW9xEs3YRv5EcpPv1eDGS7GiNYDEjjSDEJhjYxCAChQqAGTLfalBoKEkKClwEjMIcBnMRdofV36ELS1umrsIXc//30Vf6LNg2bWCIBQK8Ze//IW//OUvHb5n16s5IQRXX301V1+dn62wAyUx8zjCYmiZ6htfKkw9O4JFKUUgFYGS+NLHlxIvCPBlQNrzUUpnfElxt5pCbmtMMlzz0MwvTglr0FTL2q1jmD49jejEWv7GG2/k0EMPZb/99ttp/V133cXJJ5/MwoULqampYdGiRd1uimaGDI74zlTu/9MKVry4aVAIlmRNaiex4moCX4BRl0JTCl2BhvpcCwwFeC3LjgRKEQBSCKRoudUEShegayhDIAwNTA3RIoaEpaOFNHTbQA/prP7gE9Y6awnZNpZhZhbL4qA5hzJyct+nE9PNzejC5Ft/+UOft5UvuGmHuKNz59zj0YRA12H6IYex3/cX5mR/Kp0m2FYNhoUwTDDtzH1dA12gpIZdVEfZZd/t1fY3//e/INq9C4V8Zea4fXEaF/PR6hXsM22/rt8wiMnfs3AeIX2f0LZPqX/kpJ2faNFdHZ2yOzRoVZJUaiO+30wstmeLgJOgFErJzIalj5ASVAAtt06imWcjIV4qK20TE1JJapIa4fGlHLL4dhQSR68AwPBHtRjKSVTLAipzK1r2g2x7Tds6kXksOnGYBVh0xBMcstv4rr4+quIew82BLWnONuufegZHjWLKCbM6fM2HH37Iww8/zCuvvPK5566//nquu+46vv3tb/Ppp59y5JFHsv/++3c5JZSqT/Pcje+xsSaNrcFXDh8cUavUtiQA+sm7M+rgjscsA4mf9vHjPl7Sw0t4+EkfP+URJH0CJ0CmA6QTIN0A5QQoT6K8AHwJvkIEEs1VCKnQlEJTZEQR7NTEtNLYSL3ezBgRIummaEw1sc1vYPnfP8FUOqYwMISOpRkYmoGlm5i6iWkYWIaJaVpYpoVlWVi2iWXbWLaNHbGxQja1tXV4hqCmciuxgkLsSKRfTONyyYxTv4sduZ/A95G+z5o1W9n48Yfk6jS5/eb7SG3vLFF4ApHydb3fgVQIa3BFJe579i6e3fJPIlqEg4YfwEkHn0H0/TD/t/LRIcEyBGhuQFEiQXjr65lzfAcKRXUSbGg79be8xlAZPw1Rt3yHJ0TL8wKEQAkNtJZboVGyfTuHmCbPFBehCw1TM9CExpiYgZQxDL0YXehU+tX4pJgQnYEudDQh0ISOLjQ0oaMJDV3T2ta1LZre9rwmNHSht7xOa3tvXbKZF2r/ip+YxD6jupeTUp2EEaHsVrW0cuGFF/LEE09QVVXF8ccfT0FBAWvXru1wfTZQUvL+Wx4jC6sp3b1jk6ZXX32VioqKtp5YVVVVzJ8/n5UrV7J06VLuv/9+IJOHMHv2bF577bUuBUvViho21qSZuXcpB87bCzM6OHxM3OaMN0Twj3Wsemwdu191MIb1+cOPpmtYUQsrR58rcAPchIcf96i+azkF9gjm/vKCtufXvf1vqjZswXVdXMfF9Tw8z8X1XLzAx/M9km4KT/q40sNXAR4+vgra//3vsQ9/WXR75r5SCCURSmWiSZrG2eecy25Ts5vXkkvKZhzFoTvYvtf88HiMLEV12yNIWhjWdoqPDKF8D3wfggAVeBAEEARY+/chj0uBsAePYNmweR3XVt7IV9Se+NLnD9V/4dZH/pe05lCR3DDQw8s5Q4KlGwRCxxo5i9CZSwd0HO8tPo6yrSv5+2n/GrAxbGqo44V//JVTJ59ELNS9KZ5tjsVBw5yuX9gLFi1a1KP12aD6tZepSo7n66fKTl930UUXcdFFF7U9Puqoo1iwYAHf/OY3+eMf/8gLL7zAV7/6VWpra3nrrbf42c9+1uW+Iy1lwOP2KR80YgVgxP4jeOPVLdi1KUYaYsByrXRLJ2zpUBLCDVIMi+48Dbf7gVPZ/cCeCwgpJb7j4SRSOAkHJ5km2dRMzfYtOIFDOp3CSadx0umM9UN9PXWeZOvGikElWHbFDwRmDxKnm+9/HGdTCqGRuS7TyNSqagIhWm41AZoAXeA7xURG1xE65uSsj1219F/TBpFgSaUzkcqzdjuDU48+h41b1vPQm/exJbmF+Yf+xwCPLvcMCZZu4CmJYQx8HrnSLUw1sFMr44rLiAZTeWzT7az8+2piVpSYGaPAilIUKqAoFKM0VEBZuJCyaCHl0UIq/RDDC/InUa+vfPD8eoqsGBPmnNGr9+u6zpIlS/jFL36B7/t4nseCBQu6VcYfG5Epi03UpXq174HCCBkcfvmBfPLn5Tib4+gDnDQrpcQjTVFRdgz/NE3DCttYYZuCYZ+tn8xX2n398tde5R//fJ6y4YO7PNiTYPRAsMQ/cEFJDKsJlEApAWigtJb7AqW0tnUCD2t8bsqOZToT9dVCg+c0OHWPvTnstQO4ruIPTPhoEvtOP4ifnf5fAz2sfmPw/KUGEAloeZB0q3QTQ3Z+Vd8f3HvyX/neEz9ni/MugZNCCge0TiIoU8GNzO2/AeYQr7mZ9dWj2H/v2k6TbdtjxwT1OXPm8O677/Z4/+FSGwEk63MTsco1Ku7hGQNfyVZb3QBCUVpWPCD7r28p9R/WzUTrfMUPwDS7f2xUyiI22aHwez/I4ah22J/rglJgmJ/7vcqWJHARHvhje0/4f2fezPfuP4/L3/wvnpz67Be+MmhHBtdfaoCQgJ4PvYQMG1MNvGCZVFbOS+cv3mmdVJKGdJyaRBM18QZqk83UpZr4dNsKHtt2DzPHfzHcJCueexlfRZh8bN/9T3qDpmvYuiDRODgFC2kf8iDJsXpLRjCUjywbkP03NjSAUpSWDx+Q/WcLLwDD6n71n1JWvyW5yqZGKq99A0WrWV8ABAhavFhQQAzzjZ/jLtsCwkJpFmgmaC339dbFBsPO3DdsMG3MqkeRZQchh80CMwx2GGGFwY4gQhFEtAh9wuSs9xiLRWIcN3wON9XdxubKCiaM3T2r289nhgRLN1AotHzwYdEtjDx1s9WERmm4kNJwIVOGfVYS+s6Kah7bBulmg4p1/yZWUESsoAg7FM5Lz5iuWPNuDSMiOoWTB64jasjUSDXvWug7ONBdiV868P5KtdUZi/mRY4Z18crc0NTchCYDtEF+dewHYNrd+3sqKVFYiH6aDpTba1FEiY1fj1keQQUSfB/lB6hAonyJbI6jR8pQMoqQLiJwIPBAptD8RvBckC5CeZlbPFCZW40kenwtbPh7p+MIKEYJG0QIpdmghTINF/UQygiDHsoIHiMEZiRz3wyBFc3c2lGEFcYzTJ7etp5FtQ9TadYwJZjE2FET++W7zBeGBEs3kAJEHkRYRB4LllaklKzdXkWhHWZYpJAXW65kL3qhFORn5Yc6ATFSxEgRNRVRPSCmS9A09h8OP/nBBR3tYsBwtm9nQ+1oDtm/dkDHEbJ1UsncVF3lGksqZMHAJwtvr6sHpVFSPjCWYYlEEnPw6fXPMTo8g8K6Epruuhdh6ghDa7k1wNQRptGyWC2NYHREDyIyfUGlMnle4f32wD7okNzsI5AoJ4VKJVHpJKSTKCcJ6QRy1Rso6YObzvQs8lLgp8FPIYI0BGmEuw2kg1BphHQQtNzHbXHr/YwfjyjntUiYvc2pnFZ6Ej848UdfqukgGBIs3SKTw5IH/zGM/BcsV7ywmH9subHtsVI6KJPHzhxHIpkknkgRT6aIp1Ik/v0iicLdaTaHkfAkCQ+ebBjDq83w9U8+IBorIBIrJBotwLAG/iT36T9fQRJj92Nzc/DrLqGwQbxp8LWQ9xIupgCvZOAT2JPJJAjJb676LRoGGga6lllM3cQwTKbvNZ2jvzG76431gpTjYPcg9yMfkX7AAeXfAA/iq+MoZaAwgc6PlVphx/2UHMfhkksu4ZlnniEUCjFjxgzuuece1qxZw9y5c6mtraWoqIi7776b6dM775nTKlhEDh3Tha4hIlGItPOZ9j2i19tVSqG8AJVOolIJgmQTr71yBmfbY1n4rQf7MOLBzeD+xfQTCtB61icyJwjNwgSCwEPXe36VMnHiRGzbJhzO5JNcfvnlnH322R0eJHrDmvp1iKCQsyf9J01OgmY3zu4l49lnVnsH/vM+t+bWuxfzu38PY87iTTutt3GJCYeI5hHVPKJ6QNSQRA3FcVOHceopvavY6QlrVzQwuqCB2IT+6czcEVbYwNueHtAx9ASnyWH1je+hBZIiwB7W/XymdDrNt771LT7++GPC4TDDhw/n1ltvZY899uC73/0ur732GuFwmFgsxp/+9CcOOKB7uUXHffMo3n61iHTa2cFzxcXzXXzfY3u8iuUrnJwJFtf3EQj+cc9i7FAIy7YJhcKEwhFC4TChaIRIJEo4FqOguCQvDef8ZOb/YGqWZPLZJ7StV16ActLgplGOi/Iyt7gOCIGxZ8deQ5dddhlCCFavXo0QgqqqKiDjtzR//nzmzZvHQw89xLx581i2bFmn41NORtSL8ODLnxNCgGUgrEIoLERnFNrLigpnYKO7A82QYOkGEhA9jLC0JwJuuukmjjnmmLbXJJNJPv30U7Zt29atPjKipbTa85Loeu/KMR944AFmzpy507qODhK9YXu6lpAo57+P+nav3j//O+dy5Cfvk0gkSKTSJFJJEimXRNol6XjE3YCkGxB3FUkfnmoYx/b3t3DqKb0ecrdI125jc/1oDptdn9sddQOzuJLCkffw7pN/QxM2mrDRNRtNs9G0EJpmo+shND2EZljoehjdCKEZYXQzhG5lHut2GM0ItTwXQtNNhKFnfDCySN3KOkrSPs22TmPUZNKsniWazp8/nxNOOAEhBLfccgsXXHABL730Eqeeeip33HEHhmHw+OOPc+aZZ1JRUdGtbY4YU8Y3v9Wx8LzxmlvRe9iYsSPhf9xxx1FVVYWmaRQUFHDzzTcTsW3qHY/la9d3ud1RsQgX/vyXPRpLf+AmMoJF38WPSZg6wowCPetMnUgkuPPOO9m8eXNbftvIkSPZtm0b77zzDs8++ywAp59+OhdffDFr165ljz06dsFVThqwBqVgaY9j9GKa5eCLrGaTIcHSDRT0OOm2PRFQVlbGihUr2l5zww038PLLL3e76Z1mZEKbvpeCUHb8Izo6SPSWZr+OmNH7ygvdMNhr7+7bS3/nN4vwpWDdqpWUl4+goHhY1rPyASr++TKSMiYde1jWt91T9FEriUSW46WnIYWLwkFKDyXczGPNRWoeaD307FECIU20wEIoE6EsNGWhYSGw0AmhiQi6CKNrYXQtgq5H0fQwhhFFN6MYZsutHcOwohihGMnK7dgiYMKP9ydS1rOTRygU4sQTT2x7PHv2bG644QYATjrppJ3Wb9myBd/3MYy+H9YcP0VZrLxH7+lI+C9ZsoTi4mIAli5dyrx583j//fcBkEFAKpEgmUyQisdJJROkk0mcVIp0OsULb72D5+dngrXb3NJuIZyd6dp169ZRWlrKtddey3PPPUc4HObXv/41xcXFjBo1qu3vKoRg/PjxbNy4sXPBkvYACxHKnmDpKErd0fpsoaTkU6+JGeHB7dvTV4YES1dImYmw9CDptrsi4M477+S6667r9naFnjkw+H6y2+/ZlfPPPx+lFAceeCDXX389lZWV7R4kdowE9YS0bGCMPanX4+sppgb/SozhmLs2AhuxcCnXEpQZaUqtgLKQYljE4MA9x3DMnBO63F5HrPuggZEFDtExA1cd1EqgNxI4w5j9jX90+jrp+wRuEt9JIb0UgZsicNMEfprASyG9NDJIEUgHKV2kSiN9hyBIIwOnbQmkk3lOpQlkEk/UIWXGfycIUkjNQfkOOB3kV5WCOXssY0pe7PNnv+mmmzj55M+7nt50002ceOKJWRErAJ5ME4vGuv36zn7zrWIFoLGxcafqOE3XiRYWEi1sP/n35dffJBLOz17CTmMzAFZBdozdfN9nw4YN7LXXXlx//fUsX76cY489lieeeKJX28tMCUUR4Z5FerqivSh1Z+v7yrp1/+Q3r17OOl3xH2OPzPr2BxNDgqUrpI8UAr0HxnEdXSnsKAJef/116uvr+cY3vtHt7X4WYemdYHnllVcYP348nuexcOFC5s6dyzXXXNPuQeKjjz5ixIieqXmlFL5oZHi4Z1emfWHRz+exedOn1NZso6a+gZrGFDVNDtuTAXUpRUVc5+HaUSzaKJnz1m0UWFBgCWK2ToGtEwuZFIZNwqEQITvM9Ol7UzZ85x5Jbs0WNtWPY/bs3gvFbOIH9UDXJzHNMNCMQsxI7k94MpAETgI/FcdPN+M7cXwvTuAk+GTrtThmos95GNdeey1r167l+eef32n9Pffcw5IlS9ptMtkbPM9HCpeCDkREe3T1mz///PN58cWMYHvyySe7vd1ACGKx7gun/iSobZkSypKp5vjx49E0jXPPPReAWbNmsdtuu7FhwwYqKyvbomdKKTZu3Mj48Z03XpVuizFcFiMs/Ult7b+57bmf8lB6E+Ok4I4ZFzN7vwsHelgDypBg6QoVZKaEepDD0tGVwo4i4M477+T888/v0RVhaw5L4Pcu4bL1B26aJgsWLGDPPffs8CCxcuXKHguWbc3NCD3FmIL+C1tats2kPaYxaY9pHb6maksFv7/vaRoc2JLUaG4yaA5M4sqmWYUIWqoajk7BrAdWUmS8hm242KaHbUkCXxEwkUlzZvbTp+ocKRvQtJKBHsZOaLqGFinAjBQAOwu+DyseRFLdp+3fcMMNPPLIIzz33HNEIp9d0T/wwANcddVVPP/88z3+/9oRtZX1IKCktPvTrl395v/2t78BsHjxYi699NJuiZb49u2g6Wx963XufeFZTMMgoQLqnBRTykcTDofRrRCmbWOGwxihMGYkjBGOfPY4HMYMRzDCIURTM6Gx47DKy9HC4T5PnQb1Hjpg9KIAoD2GDRvGMcccwzPPPMOJJ57I+vXrWb9+PYceeij77rsv99xzD/PmzePhhx9m7NixnU4HASg3yJQJG9mt8Nw1Sl1eXt7p+t6wadMbnPPcD1DAT8oP4txj/4RlF2TpEwxehgRLV8igx1NCXYmAeDzOkiVLusxy3xV9xxyWHpJIJPA8ry08fd999zFr1qwODxLTpnUsADri37VbAJhQ3L0uzu3RWVXI22+/zY9//GMcxyGdTvPd736XX/6y62TEkWMmcuPPf9juc0opUuk0qUScW696nyDqM2V8AjcV4KQVjqPh+jp7TdxK4diBnw4CkFoDpjZ43C0DVYsmem/QduONN3Lffffx3HPP7TS9smTJEhYuXMhzzz3X5dV2T6ipypjKDRvefVHYXeE/d+5cfvjDH1JXV0dZWee5XvEtmd9TUSARmiDludQFmaTL9dVbEAoCoQiEQPXEhFEpNKXQpUIHdAS6aFm0TNd2XdcxdAPDMNANE8O0MCwL07bRLRszFEImokzgANxP1tFYV4MWDqFHQ4hIGGN4MWZZz0+wt912G9///ve59NJL0TSNRYsWMWbMGBYtWsS8efO49tprKSws5K677ur6Y7r+57xM+kp7Ueonn3yyw/W95cG3/x+NmuDFrz/IsGGDtzlmthkSLF2gZIAUAq0HgqUrEfDAAw8wY8YMpvawS6vZ0l1UJut69D6A6upqTj/9dIIgQCnFpEmT2q76OjpI9JQ1dZkD7B5lo3v83h3pqCpk/vz5XH311Zx00kls376dqVOn8o1vfIO99uq4TLIrhBBEwmFCtk3EF0S+MoLZF36tT+PPNUJrwtTzK8LSKfp2TG3PXr118+bNXHLJJUyaNImjjz4aANu2eeuttzj33HMZOXLkTjktzz//fJdCoCvqahoA2Lq5msCXRGJhykeVEivsOFejs9/81q1bGT0685t49NFHKSsr61aifVNL0u6xP/wRY7so1w58D6+pGbe5Ca+pGS+ZwEul8NOpzG0yQXzlSkRJMdK08J0UXtrBd9P4jovvufieh++5BIGP7wekPJcgnSJQEl8pAiUJyBhpBkJQHh7PhFEH4H9k0/yRAzhAI5A5bo76r/0winsmWiZNmtQ2dbYjU6ZM4Y033ujRtpQnESK7CcvtRak7W99bDt/9G9z1/h+5/flLuPT0pejGwPtQ5QNDgqULFC0tyHvodNuZCLjzzjv5wQ963vwrHN8GgFG3tsfvnTRpEsuXL+/wufYOEj1lU2Mm7D9lWM/FTiudVYUIIWhoaAAyESPLsrpdYdUV1XUpbCUoLc9NZ9hsollNWPrAWMr3FKUUmlmPrfduumbs2LGoDswSPS831TNaS1n3K8uebVunqxC/uuqyTt/X3m/e933OPPNMUqkUmqZRXl7O448/3q22FM0tDRILu3HxoBsmemkpoSz9HrpCSomfTBLUNkPSRSZTyEQamUrT/PJahDYB5Q5sdZPyJELLniN0R1Hqjtb3hQNmfo9fVb/Pb6pe4L6/78f9B13F9Kmn9fETDH6GBEsXSJUpDRX0zJuiMxHw+uuv92os3vBMRCZZkJ8dXrfGq0FalISyl+S5Y1XIXXfdxcknn8zChQupqalh0aJFfSrB3pGKDU0AjByTnwmOraSTcTTDwbYHpmlfT0klGtHMNOFQfv6fbY8jTziQvWZNJt6YIJVI89TTT5NwGrt8X0e/+bfffrtX42iuz3j+PPqXv2DpBpZpYJkmlm2z2z77MLmXlXzZQNM0rFgM2kkIdjfW43wKekF2q3N6ivJVVgVLR1HqzqLXfeGs42+i7PXfsWDNPfzn61fw5NjDiMQGd7PMvjIkWLpAyUx3ZK2HgiUXaHZGCPgMfMfm9njl03UYBQVZa2q4a1XI9ddfz3XXXce3v/1tPv30U4488kj233//Pk0JtVKxNhN+//iBd1j/j7cxLQ0zpGOHDeyoSShqEY6FCBeGCReGiZXEiJXECMfCaHr/uZA2N2SiWKFIdiIsXbkc33XXXXzve99j6dKlnHLKKT3efkPtZgCiBX2bJuxvykeWUD4yM+324nNRAr+HnjZZYNJ++7P+0aWkpKTR8/Ed8IQgbZqsffbZARUsnSHTLirw0MID24JB+SB66kXUCZ1FqTta31eOOeRSHiidwnffWMjCpadx43n/ysl+BgtDgqULMrO2+dH80DAy5XnSdwZ4JO0jjCakl53oyq5VIbW1tSxdupT7778fyBw8Zs+ezWuvvZYVwbK+fisbTIP9giTJJg0Z6CipUAhAb5kL94Dmnd6nlMp0cBUemhagaRLNAE2TGJaGYesYpoZhaVi2jmkbmCEDK2xihy3siIUdsbGjIUKxEOFYiFBBGDtktes4G2/KTAtGC7JzpdWZy3FFRQV33HEHs2f33p6+uWErAIUlg0uw7EgqnSAc6v9owfiDDuS7Bx34ufX/c9lleWnV34pyApADb3anfHDT46i+4n4QChBE9oCC87410EPrEaXFEzlQL+Rdv2GghzLgDAmWLpAtERY9HwSLmakSCoL8FCwF0SSlWQj9t1cVUlJSQjQa5YUXXuCrX/0qtbW1vPXWW/zsZz/r8/4AtiSTvBlWLPr9OZ97zvdcko0JEg1xEo0Jko0pkk0p0vE06YSPk/Rx0wFeOsB3A3xPIQONVNxDNgYopYHSM7kYbZ2/W5MUO0H5IHyE8BEiQNMlkRGrGX1IdgRLZ2ZnUkouuOAC/vznP3PJJZf0fh/xSgCKy3qf1zTQOH6Ksmj+5AwllGJULxr6tefGOmfOnD61C2mPfBEs0UMmor3zadvjRPUkUp9uZLAUB3/070e46o2r+aQlSvTdwr4l8n4RGBIsXaBapl96mnSbC1rLmvM1wuKqBoqtKX3aRmdVIUuWLOEXv/gFvu/jeR4LFizg4IMPzsbQqY57lHRg12CYFoXDLAqH9b0yRwYBbjpNOp4i3ZwilUjhJBzSCQc35eIkXdy0h5v28dI+vhvgOQG+q/A9hW7VAFBQ1Pccls7Mzm688UYOPfRQ9tuv+20S2iOVrEKqMHaeurV2hQwkPmmKirLTCiMbpDSNWLR3EZ/23Fj70i6kPZQnUSp7uSO9JXT4kYQO/8wZVv7uf5Fu7jo3Z5Omxk18740rmIDBHyadzb7Tzhgqb2ZIsHRJa4QlH6aEdL1FsORhhEUpRaA1MSzctyvRzqpC5syZw7vvvtun7XdEXVoxLJT7PCVN1wlFo4SiUehF4cyrT7xEytWxQn1PDu7I7Ozhhx/m4YcfzopzrOtuQzKISrB3Yfu2ZpQWUDKseKCHAkDguqQtq0cuvD2hp+1C2kP5Euj/nJ8ukRLy4DjeHapqPiSpCS6d+VP2mzF3oIeTNwwJli6QqjXCMvBJt60RFhUMfLh1V6qamxB6mlGDNIu9PjDZKzbwf+OucN06pJGdZMaOzM4++OADKioqmDx5MgBVVVXMnz+fyspKLrrooh7tww+2AflT0dSwfROrVt6DrtkYZgGmGcO0CrDsAiy7EDtcgB0uJBQuxDBCrF+7EYBYUX7Yuzdu3gxCUDisdxcGnbmx9qZdSHuoQKGFhrNxwaNket1LBBKEIrRnlPKL+rb9XiMFQsvPgoVdUS3nHdMYHBGh/mJIsHRBa2izp92ac4FutgqW/GsxvqYuk1w5rnDwlK+24vs+TSLE6I7mhPIIP2gAkR2vmI7Mzk477TR+9KMftb3uqKOOYsGCBb2qEpKiFkT+RAQ/eudvpPS7kL6Nbnbe4kJJnQ0VM4DpFJbkx4mjcXOm6qqoF20IunJj7U27kPYo/vpXaHrp32i2QgUKAoVSoJwC0mvi3d6OlBLpB2iGnpUkYyU1sDpo0JlnpNINwJBg2ZUhwdIFbVNCeVDWbOj5G2FZX58RLLsNwmqQTVu3IYXOuGH5n2cRqCbMLKYNZsvluCO0UAVK5o9Lp+NWEcgJHHPCs3hpn3SqGTfVjJNuwnGb8ZxmPLcZz880b4yEM55Ju00aN8Ajz9DYUsVVNK7n4+nMjbW37ULaI3b4PsQO3+dz6zdd0lqt0z3+dsMdVCQzSdtTCidyzs/m9WlcSmr0oCXcgPLa2seJSsUek44d6KHkFUOCpQuCFuO4vEi6bYmwkIcRlo2NmQPL5EFYDbJuU6ZUeOKo3icadtYD6bvf/S7vvvsumqZhmibXX3/9TpUZPUJLouvZE4XdcTl+6aWXer+D9MEoqrp+XT/hB9sQlGOYOoapEy6wgY6nV6ofS2Oa9VjWwJqgtdK0fTtCqW653+5IV26svW0X0iOkQBjdFyxN6Thj7eG4gcuGxi0s+dPf0DQNITQmTZnErON6Vm6vpEYeBMq7xPfSPFH/IceEhmOa+e+83Z8MCZYukLJlSigPpLmmGZlUtjwULBWNmbn+6ngdowrzp6KiO2ysyjiK7jG+900boeMeSH/84x/bThTLly/nmGOOoba2tsdhbqUUwkxjWfmTE9IVUtWiif7r3t0VSqvBFF/p9uvjzQlCofxJIG1ubMR2XfQeTtt05cba23YhPUIJ6MFhVKEoLypl1NgxLF+5grp4AwpJs59kw5ubGT1+LCVjh2HFujdtoqSB0PMrh2XTpjdYsfYJmp16TM2iKFzGCxufZ5MOf9jvJwM9vLxjSLB0gZSZg5Xek19aDvGFQMmBLxnclT1LJ/N2IxTYg++KYFNdE7qCkeXFvd5GZz2Qduww3NjYtcV7R7ipJEbIxw4NnsRmo+AjdOfrOdn2uo+fpab6bXQ9hG5EMPQwhhFGNyOYZhjTjmJaMSy7ANuOYoYLEGYdltZ9ARWPO0QiAz8d3Eo8kSAie37S7cylFXrfLqQnKDS0njrPCsGBJx3OgScd3rbqhf95nFc2v8Ot9/8P40Ij+P5l3UsEb5QanltNcsXzjJx+OLo5cFOV9dvX8bunLuAJP9MvylIKH5BCMDxQ/HzEwUybcnLnG/kSMiRYuqBtSqgd19GBICA/c1iqEttQSmdiSXnXL84zKhvTFCmy6h66Yw8kyDjKPvjgg9TX1/Pwww/3al+NdZvQDEU0OjjyhFrzv5SVG9vydRVXoNm1SCeM0F00vWshr1sQscd2ex/JpCQeD/Hmm/di2xFCoSjhcCHhcAHhcBHhcDGGEclaO4quSLgukTx2ue0cDfTuf08Zn+nPv/7I877G7p9M5dEn/sGmdDU3XX0DGhpCwKjikRz/7a+jmwaGaaKZOrqRudh83FhPUzwNj77KiWs+4MAzf5q1T9ZdZODzjxcv54+bnkICV449jq/N/gWxglF4XpKmxs2Ulu6BGLR/49wyJFi6QLZUCeVLhCUQgpc3vcA1Sw5o+SkLWo+Vgs+Sg4UQCASnbPomI71xKE1DoZCaQgqFEgo0ga5nftC6bmLoBpqlo4dMjLCJEbawI2HsaJhwNEokFiMSjWG0YxtfnahBCwrR82DqrKdsi/uUmNk74ezaAwkyfZCuv/56nnvuOX75y1/y2muvYVk9u8Jrrq8AIFqYHwmgXZFoylw9FoXPysn29VDGRG/Ose/jexLPcXGdJJ6bxHOSuE4cz0vgeQl8L47vJ1HKY69Z3e96GwoJGhs1nn56dYevESJA1wN0XaLrktGjw3znO1f1+fO1R0JKysMDX2K97uWX2fjRRzTU1LI5lURDoAvQhUAXAkMIdE3D0HQMXUPXdZRdjInJqr8/hWHomKaJYRroholpGhhWRmSYtolhmjTLFO3pQN02mDBzD07hZD54871MNZFUvF+7itq6Jlb+eZe/lQIdjUBIDhw3mXc3/Rsl+79aaNWaJ7jmtV+xQnh83Srn58f9lWHl09qeN80IZcOG3Gw7Y0iwdEEgWyMs+XEi1jWYbFtsjA5DoVCwg9HaZ49Vy6NDth1EfaSJeNRHUwIRCIQSaEqAVCAVSgJSIZSHGSgigUFE6ugoIAkkCaijmUwnHYkkpTukdRfH8PBMn7OCsXxNfptn/vg2WBoiZKCHdcywiRk1CUVNQgUWsQKbaKFNrNDGMvPjO611BaPC2REsu/ZA2pU5c+Zw8cUXs3Llyh67yMabMnlCRaW7ZWWsuaahLlOCW1wyq4tX9h6b49F0DUvXsEIGUbI7JXnRRb/D81Ikk42kUg2kUs2k03FS6WYcJ4mTTuK6Dq7r4HkO69ZtYuvWrA5hJ1KaRqyd/1f9yboXXuD/e+UVhJSEXZdRhoEhFYGS+EFAoBRpBX6LT3iAwBcgLR+pGQSrqwmQBKLrqS2zk2mbCTP3YMLMPdoeH7e9iU/fW00QBAS+T+AHyEC2PVbAzGP25+2/rmF1xRbe+c0lFNmgawJDFxia1iauDF3HMHR03cAwDQzDRDcyQsowLQzTRjctDCtEgE5TymfUyOGEIjHsWAmRss+ioL6X5n+evIBF9SsYLzX+d9YCDpj1/b78Cb60DAmWLghaemKoF6+i4d2bAXYJUrZ/olO7rlYt/wjR+mDn9UohVEZmCJV5nFknW+5nbqOB5IgRe3Py1x/p1vg3vvUsZfs47Hty9xt+eYFH3I2TSMVJNjeTTqZwEkmcZBo/5eInXaTjo9IBOBLNBTsZZlSqiEidQ0gqQhKigNHO95NsWRIoUkJRE7jc5FVia5KoDgUGRC2NQtsgZpsURC0KojaFsTCFhdHMUhKjqLSIWGEMvY/dkhsCk1kFfRcs7fVA8jyPDRs2sMcemQPr22+/zbZt25g0aVKPt5+KbwVr8ERYmuo3ARAr7F7OyIrX76G69v8DZSMIIwihiTCaFs7c6mH0ljwVTQujpCASntHldo877jiqqqrQNI2CggJuvvlmZs2a1WWn6lZMM0xRUZiioq49hhYv/hVbt3o0NGwhHC7GNMNZm2qUvk8qhy633aVuU0aI/vTiH1E4vPdTwDKQ+I6H77h4aQ/f9fAcL7PO8/E9j7HTuy/Oo6WF7D1n/y72GTAx1ERN2sBRBrqbpsCCpC/xZYCvIJACX2WWQGn46PhoBD04XX7/Gwczbv/j+fDjB/l/b1/PChwuKJrOD0/8X0w7PyrOBiNDgqUL0lbmYJMKKTRvRwOsdkKKO1jKf/7017qm/XlZJQQIgRIaSgiUpmfEjRAZO2mhgdBpLBqBPaP76lxpEun3LEnX1E1KwiWUhEug95W+SClxUj5NjWkSTS6pZpd0wsVJeHhJDz/pE6moZVoqxFTLY7uviLtQ5+gkEhDXBElNI2VoKBEA8ZblM4SShAOXaOARVR5RAqKaJKZDzBDEbJ2YrVMYMolFbApjoYzwKYpQUBgjVhihWYQYVdy3k0pHPZBefPFF5s6dS2NjI4ZhEI1Geeihhygp6bldfSq9DakLDGNwtG9znUyC8cpVX0N+bKL8MCqIoGQEoSIIomgiiq4VoOtRUv6/wGxE8/ZBqTSSBiRVoBwQaYRyMot0EUohNCgu7Vr4LVmypE1ALl26lHnz5vH+++932qm6tyilcJwQf/rTHS1rJIaRmTIyDIlugGmAYWiZCxN3BKY2Csuy2hY7ZBMK2YTCNqFwiHA0RCQaQjVsQ2kaBX3o85MNNlVXEU6liBb27f+hpmtYERsrkh3n5u7tU2feZTf26r0qCAjcFL6TwHdSBG4S33FIJpqorqmjNGpSs3kdT33UQF3DOm665wieCerZXQr+Z9+fccDM72X503z5GBIsXdCa065/7XoKdz9vQMfSKwRo75exedmrALhjNzHp4m/3y641TSMctQhHLeggT7Tqhkfwmgu44YaORViQdmhqaKJpexNNjQmaGuM0NSdpijvEky5NKZd42qfZCYh7koSvaPQFWz1BPKWR1AwSuomjG4APbZNb1ZkdCI2tL/0fNz/xFwzAFBqmpmMaBpZpYVoWlh2mcPgIDvnvK9u9Yu6sB9Jrr73Wk6+tQzy3FsxwvyV49pXp+5/B6g9iOE4DnttEoOL4NBOQIJBxlEoQsIVAJUEm0CPb0dRovnrC3Z1uVymF7zkEgUco3PVJc9cqLSFEp52q+8Jpp/2ITz99C8dJkHZSuE4Kx2mdMvJwXQ/PC/C8gFSqATel4TsSqXykCpDC79JcrWT0wHkdKaVY29DAZMNE70XH6MGM0HWMcAwjvHMfrzKgNeYplj1D/Zon+WXFg3ia4OoJJ3LSkdegG/ljnjiY6ZFgufXWW7n11lupqKgAYPr06VxxxRWccMIJQKb7689//nP+9a9/4TgOX/va1/jzn//MiE5spH/9619z1VU7J6hNmTKFf//73z38KLnBlxnPE10zB3gkvSMobUKv+cwXxdiaX9b5sslF+YlOX6OHbEpGllMysm8VSG4qTWN9E00NzTTVx2lujNPYnKRy82amFYwmSBbipJK46TSuk8ZzXdJOmuZkgpSsIb1tMzO3biE2dmCmZHxZjyEHTzjZsiN85YDuJ7g+/8JkCou7roASQmBaIUy6f8I8//zz2wzynnzyyU47VfeFwsIRzJx5Urde+8yTBxAWX+OIE36+03rX8UjG0yTimW7eqWSa2tUb8O/8Pfqsg5hw6CF9GmOfUApPCGqUZMPrrzNu9uysVtcNZuqa6/jNx4tZNnolkwONm469nXHjstNNfogMPRIsY8eO5frrr2fy5MkopVi8eDEnn3wyy5cvZ+LEiRx33HHMmDGDF154AYBf/epXfPOb3+TNN9/s9D/19OnTee655z4bVB97WWST1hyWwSpYJlx8KlKmMM1C1t12DyKRX59DphSin3rNWOEQ5eEQ5aN77mPy+rVX8ebyt4mM7Ju5XF9QNKOR/+0DeoPvJwFJOJwbMdhqkrZ48WIuvfRSrrnmmnY7VX/00UedXmBliyDw0K0GQvrn/z9ZtollmxSXfRY92lxdQ/OWLZT++swBFQhC0yj0PCojEe569lnO930mHXFEr7f35JNPsnDhQqSU+L7PL37xC+bOnZtdd+h+4I01b/DLf/2SOHG+XXI8v/jGbzGy1KR0iM/okTL45je/udPj3/72t9x66628+eabbNmyhYqKCpYvX05hS1LY4sWLKSkp4YUXXmDOnDkdD8IwshKOzQWtgsXQchfS6+hHmw103UTXMyJFJHWI5o9rJ4DydYSVX2Nqj3hdHSGl0AZITHuuR82qk/GTY7h72b3ohkQ3FPUbx6BbHiP2rEE3BLop0E0Nw9DQLQ3D1NFNHcM0MK1MlYNpmRiWhWFlprtMy8a07UzUwg5jWna/V8U1N38IQDSyRxev7Btz587lhz/8IWPGjGm3U/XKlSv7RbA0N1YhNEmkm546qU2Z1hex3Qa+9cX8//ovPnrySf5v9Wp0q/cnZaUU3/nOd3jppZfYZ599qKioYOrUqZx22mlZc4fONUop/vTcn1i8eTFFFPE/R/0P++3Ws+q/IbpPr4++QRDw4IMPkkgkOPjgg1m3bh1CCGz7s//AoVAITdP417/+1algWbNmDaNHjyYUCnHwwQdz3XXXtTXqag/HcXCcz67Km5qaevsxuiRQLREWkZsTVWc/2oKC7CZXimQIhnfeoba/UcpGj+TXmNoj0dRAeIBK26WUPPCbx0jU7M/wKZvQdfA9CFpyqQPXpLFKQwYaMtCRvo4MDFRgIAMT1I7jloDTsnSM0DyE7qOUhmGlOPYH5ew27fBO39MXGptWABArmNb5C3tIQ0MDyWSS0aMzwuDRRx+lrKyM4cOHt9upetq07O6/IxrrWiuouidAnK1VCGEQHj3wbRlCxcWYLR5CBSP65roshKChoQHIHMfLysqwbXsnj6K+uEPnkngqzo+W/oh3vHfYP7w/N59yMwWhwZEQP1jp8Vl45cqVHHzwwaTTaWKxGEuXLmWvvfaivLycaDTKpZdeyrXXXotSissuu4wgCKisrOxwewcddBB33303U6ZMobKykquuuorDDz+cDz/8sMMT9nXXXfe5vJdc8dmUUO4iLB39aLONnopBYc+iGZ2FbF977TXC4TCxWIw//elPHHDAAT3atpQSYcbQC/O/5XsimSBsDUyS4ZO3PE1jTQmT9w847oKeVxr4nofnpvDcNF5Lbo7nOviOg+e5+G6mpNT3PHzXx/N8fC/AdwMqlgekG4bz3j8/yalgSSTWAFBY8Pkuv32hsbGRM888k1QqhaZplJeX8/jjjyOEyHmn6s6IN2XMWgpLu+e661VvQ4SLcxZhWP3MM7z0wgtoosWPRNNQSjHzgAMJxWJMOuJw9B1ERKI+03+rYFTvp0iFEDzwwAOcdtppRKNR6uvreeSRR9rESjbcoXPFp9s+Zf4T86kRNVww7gJ+csxQ35/+oMeCZcqUKaxYsYLGxkYeeugh5s6dy8svv8xee+3Fgw8+yEUXXcTNN9+Mpmmcc8457Lvvvp3+R2tN2AXYZ599OOigg5gwYQJLlizh+99vv3Lk8ssv52c/+1nb46amJsb1ot16d/BbBYueG8HS1Y82W3jJOFoQQhR139a/s+jPqaeeyh133IFhGDz++OOceeaZbcnY3SXYVo/QLYyyniWS/vjHP+axxx5jw4YNLF++nJkzZwId+21kg5TnMrKk466+ueKNR16j4iOb8nGNHHfBqb3aRsbsyiQc7Vn+y2v/9w/SDZmLhqbqAla88iymbWGGQpkS3FAEKxTBDkWxIzFMy+p1BZOTrgQEltXzcu/OmDBhAm+//Xa7z3WnU3WuSMa3IgODwtLuRShkXS3Esvvd7MiqZe9QbZqMDQI8KdmsafimyboVmbYK5osvMFFkjNUMXacukchML/ahUsj3fa655hoeeeQRjjjiCJYtW8ZJJ53EypUrGTZsWFbcoXNBY7KR7z7+XdKkuengmzhq6lEDPaQvDT0WLJZltZlg7bfffixbtoybbrqJRYsWcdxxx7Fu3Tpqa2sxDIPi4mJGjhzZI5Os4uJi9txzT9auXdvha2zbzkkEoj2ClkaDRo6Sbrv60WYLpy5TwmsV9+yk1VH056STPquEmD17Nlu2bMH3/R4lTDsVGd8Lc1TPDsRnnHEGv/zlLznssMN2Wt+R30Y2aBKK8bH+DfeuevMT3n0mTiTWxJmXdb/aJlukE5/59yTrRvDavZCZUmq1/mvY+Q1CohkOmuGhm37LIjEsiW4qDAtMW8OwNSxbxwwZmLaJ0Byqa3wiwwZPU8ddWf/v11m96vcIbAQRNBHJeMzorUsM04xhmDFMs4Cmpo8IKMUwujfNqOproSh300EpJ02h7/Pd669vWxevrsZpauKpxYtJ+j6NQOAqfCEIhKCvl4grVqxg69atHNGStHvAAQcwduzYtgToVvriDp1tlFLMf3g+jaKR24+4nQN3P3BAx/Nlo8+JGVLKnfJJgLYT7QsvvMC2bdt2Orl1RTweZ926dZx3Xn54ngQtZc25Srrt7o+2r6S31wFg9cCwrLvRn5tuuokTTzyxx9Vd3pZMWNkc17Ny5SM6qEpoz28jG6SaMnPodU4yK9vrDtXrq3ju7rUYhss5vz4RrZsntmxyzLdO55hvQeAHuE4aN53ETSdayr5TuGkHz01nbtMuruPiOT6eE+A5Et8N8F3wXfAcQbpZJ/B0As8g8Cykb6ECi4wn8nyKJqyEE7saVX6yqeJptPBqVHofFLX4IgVaCiEyi6a5GVOnAEiT+chN3Z/+0pq3o1LNvP+z69HCYbRoBD0aRY9GMAoi6LEoZkEUszCGWRTDLIxiFUYwutn+IuX7hHYxtIyNGEFsxAi+c8013R5nTxg3bhyVlZV88sknTJs2jbVr17Ju3bq2C9ZsuENnm58++FM+lh9z8cSLh8TKANCjM8zll1/OCSecwPjx42lububee+/lpZde4plnngHgrrvuYtq0aZSXl/PGG2/wk5/8hJ/+9KdMmTKlbRvHHHMMp556KhdffDEAP//5z/nmN7/JhAkT2Lp1K1deeSW6rnPOOedk8WP2ntYIi5ajpNuOfrQ7fmfZQKYzU0Hx9esoGj+9W+/pTvTnnnvuYcmSJbzyyis9HpO/rQmlolgTslchtqvfRjZIbM5YkU/9Su564uxIsjHBIze8Dpic9otDCRcMrPeKbuiEjSjhaBTIbjfuwHdxnQR/v+pFwtGirt+Qp3heJVKN5/iT7m/3eSl9fC+B4zTjpBpx080UlnTfdl7tPh191XLEc48g/DSa9FBkbBA78rFWCKRuEegWSreQpo0ybJRlg2mDHWLL8GLiIwpYb9vs5vVvF/gRI0Zw++23c9ZZZ6FpGlJKbrnlFsrLyzn22GOz4g6dLRzP4cbnbuT51PMcpg7jwqMvHLCxfJnp0Vl427ZtnH/++VRWVlJUVMQ+++zDM8880xYJWLVqFZdffjnbt29n4sSJ/Pd//zc//enOLbxbp4xa2bx5M+eccw51dXWUl5dz2GGH8eabb1Jent0DY28JWrs1a7mZguroR9tZlVRviI4di8tGjFj3p4S6iv488MADXHXVVTz//PO9KgX1G1IoV6KHs/fd7uq3kQ3REt+UaTpYvkfuO6kGfsB9Vz9J4Bdw3PcmMnxifpb7ZwvdsAgbFjKAcEF+eQT1BF9tQ1MdT2lpmoFlF2HZRRQUdi/Rdkf2u+/mnR5L38dvTuI0xvEa43hNCfzmliWeIIgnCZIpRDKFSCZR6TQinYJUChwHnDS4KdYWD8cRkoK0NyARjHPOOafdi9NsuUP3ltfXvM69K+5lS2oLdX4dTaKJQAuYHZrNX878y4CO7ctMjwTLnXfe2enzrUlSnbFrYub997d/RZIvtDrdWjk0AeroR5tN0nWZfJHQsO4Lwc6iP0uWLGHhwoU899xzvRZXstkHmRsPlla/jbq6OsrK+jb337w1E2EpnDgxCyPrnIeue4xUvIT9vhZhz4Om5nx/+YCUEj8dIVqU/9ViHaLVYIrsRkU73Z1hYJUUYpX0zUjwySuvZ+LoPTnvwv7PkcpHpJRc9thlPNX4FOEgzGhjNNNi0xgbG8sRk47gyKlHDvQQv9Tkj6VsnuIGGcFi64PHEr09nO31QJRwefev7jqL/uy+++6MHDmSk08+ue31zz//fI/EgXQEQs9OGLojv43SLDSKi1dXI5SiYPyEPm+rM56541lqtxSx295pDj61Y9+iLxqpeAMqsBisM0JSBmh2HbbonglcvqCUQuIRCYcHeih5gZSSny39Gc/Hn+cbxd/gyhOvJGR+ufol5TtDgqUL3CBjamYbkQEeSd/w6pvBBCvSs7NCR9EfLxvz3dJCi/R8OxdeeCFPPPEEVVVVHH/88RQUFPD888936LfRV+Lba7GlQsthSeU7Ty5jzTsaJcPrOeE/ele+3B3q6up2sjhPJpN8+umnbNu2Dd/3Of/881m3bh22bfPXv/61wwTnbNK0PTNFHOthBVu+EG/chqb7RCM9n+rpKXfddRff+973WLp0KaecckqftuWkPJQIiETz89gmpeTBdx7knk/uoVk2E9WiHDjsQH585I8pyXKJt5SSHz/8Y15OvsyZ5WdyxYlXZHX7Q2SHIcHSBa0RFlMf3FchQaODCA+MU2uH6FG0ArfHb1u0aFG76zvy2+gryaZGQiJ3plWfrljHm/+oxQ7HOftXp+bUIKusrIwVK1a0Pb7hhht4+eWXKS0t5Xvf+x6zZ8/m6aefZtmyZZx66qmsX78e08xtbknT9u0AFOQwqXLZM7dSX7UZ0w5hhiLYoQhWOIoVimKFC7DDBdjhIqxwIeFoEYYV7fbfob62IjP+4tw2xayoqOCOO+5g9uzZWdlew/ZmAGKF+Rc9Xle9joufupjNYjMj5Aj2CO9BnVvHwzUP838P/h8X7n4hPzjiB1nb3+LXF/Ny8mW+PeLbXP61y7O23SGyy5Bg6QJfuuiovHJZ7A2qGYj2XBzkCr8piTBCGKX5d7DclWQiTqgPPVM6Y/vWWp667SM0LeBbVxyHYfXvT/LOO+/kuuuuAzI+Nq3+RwcccACjR4/m5Zdf7rStRjaINzQBBsU9yK/qCYHv8urd/4fQQAHK78ZvWSh0U6GZYFhk+jLZOoZlYNgmpm1ihmzMUIhkaiNmcZSSfSbmZPyQiQBccMEF/PnPf+aSSy7JyjabWgRLQZ4Jlhc/eZHL3rgMgIVfWciZ+3/W8PHfW//NFS9cwc3rb0YqyYVHdr9ax/EcFr2yiOXbllPpVFKtqjEwGK4NpzFoJEJkSKzkOUOCpQucwMXIkp/HQCISFmK4HOhhtOG2msaNzN/EBaUUH//f/1ExYhxuKMzVv1q40/O7/q/IPFZt98RO9z97Udt9BZH6fdGxOeWnsygo7d/v4vXXX6e+vp5vfOMb1NXV4XneTk1IJ06cyMaNG3M+jnhjEqGHCHczzL/2/Wd47+kH2L6hGS/lM/Wr0zKuu+EodjiKFY5hhwuxI4XY4SKcVANKahx1wUnMPHounpfETTXipJoyS7IZJx3HS8VbvGZallQSL53KtDNIOXiOi+d4pONpEnUJfFcSuAo3riG0cZx0Xu4qG2+88UYOPfTQrBqnNTUmACgsjmVtm30h7aW5+YWbuXfrvQwTw7jj63ew2/CdS7+njp7K/d++n9PuOY37P72/W4Llw00fcu/ye3m59mWa9CaGyWGUm+XsV7gfnvRY17QOXdeZP2N+rj7aEFliSLB0gSddjMGvV9CSUURh/jQZ9DZl8hassQPfzK091j3/PM8/8wxbIxGKdYMxJcWIcAhURsiozJ3MFbva5X7L7Y73P1sHkHkulTIw/GLGzJKM2TO30wntceedd3L++ef32PAv21T9exXp+ne57T//jNHqgBuyMEMWVtjGCoWxwiGsSGYKZ9Vrr1G/0UG2REo+enY5gaeB6vyHOmqPWWia3eKUXUJBcXbGf98Vf6D60/fR9dxEYT/88EMefvjhXnkddUZzUxyAotKBbdhX21zLPW/dw5KNS2jWmtk/tD9/PvXPxELtCylN0zh9j9P5/Zrf88baNzh4j4Pbfd3b697m96//nlVyFYY0mGZP46L9L+LwKbnriTVEbhkSLF3gBR76II+w+E4Cwy1ElOTPtJZX3QCEsSblV2XFljff5J9Ll1IRDlOkaZw0fTozTz89J1OC99zwDA0EHH5Kz5pGZoN4PM6SJUtYtmwZkMltMQyDqqqqtihLRUVF1v2A2iNZl0IIGDV1FG4qhZtycNMpko1x/LQkcMF3QHqf/Q1G7V3Etxfe1/Y4CDw8p4l0sp50sgEn2YSTbMRJxXFSzdihGKMm5OZ7TjTWYYZyFx179dVXqaioYPLkyQBUVVUxf/58Kisrueiii3q93URzxrm5sHhgpoQcz+Gqp67iybonCbSAPc09+cPBf+hQgOzIGfudwU2rbuLud+/+3Ovf3/A+1796PR/6HxKTMS6YcAHzDp5HUQ8LDobIP4YESxf40kcf3HqFVF2mM2xfPRuyiV+XRDoeelF+jKn6rbd45pZbWL/bbkQ0jeMnTeLAb38bPYfRByklAhNU/wvJBx54gBkzZjB16mdeL2eeeSa33XYbv/71r1m2bBlbtmzhyCNz7zvhJBKEC8dy6k9u7PR1Mgjw3ATpxHaiRTt3CdZ1Ez1SRijS/xG7dLyBcEHuGmNedNFFOwmTo446igULFvS5SiiRSCCUnvOk6vZoTjUz78F5rJFr2C+0H5cdeRlTRnffxyZshzm+7Hge2/4Y5917HsPsYQQEbIhvYL1aT0RGOG/MeVx81MVE7Pysghqi5wwJli4IVID+uWyFwYVTWwNAqCw/3IMBgiYXAjdr/X56S9PWrTx/552sdF3MMWM4yHU55te/7lMX2u7geT5NFRpGcZri8v7PIbjzzjv5wQ92rrL43e9+x3nnncfkyZOxLIt77rmnX05mbqqRwm74A2m6nslNCeeHyG3FSzcwbFz/mcZli2QygSH6p4nsrly89GLWBeu4fK/LOeeg3plmXvX1q+AJ+Ffdv1ibXouGRolewpnDz2TB0QsoCA/sVNcQ2WdIsHRBQ/NHiA67dQwOnO0NQAGh8lFdvbTfUCmyZhrXG5KNjbzyv//Lu7W1KCHYr6iIr/7gB4R3aKCYS1a88W+ENPnKESNzlvvQGa+//vrn1o0YMYJnn32238cSuM1Eivpu8NceTz75JAsXLsy46fo+v/jFL5g7dy7XXnstixcvZs2aNTzyyCO9jlYEvo/04xRksbN6V7z00ktZ2U4qnUIFgtUr1zNhjzHY4dz5DO3K6vRqDis4rNdiBcDQDX570m+zOKoh8p0hwdIFUqmsxVc+rHyR+z+8jYQfp8QuoTwyglGxMYSNMLZuE9JtIlYRheGxlMV2o8DOzLkqFSAV6FrvfFT8hiQYBmYkf644VGCghftfsPiex6O3LeLDukzS796myZwLLqBobO5Nv3Yah5Op2IoUDMwVbr4Q+AEySBDLgiPxriil+M53vsNLL73EPvvsQ0VFBVOnTuW0005jzpw5fOtb3+J73/ten/ZRt6UWkBSP6LiPUL6ilMTXE9z78GJGFOzGRZfM7Zf9+oFPSqQoj+RPxHeIwcGQYOmCSlXKNr+WfRZ/5XPPaYAmdi5v3bEbilI7Pw4QRDUYZlk0+5to9CVBN+SQJRS+Eoy0LUaGoqAkEp2QEaLYKqDILqbYLqbILqDAKqDILqc4MppRhXtSHh2NbHIhHO/tV5AbtCh6YW76CLWHUorl/3iMl956kyY7IxLO+9oJ7D77oH4bw45M22833nu4lrqqxgHZf75QX1kHSIrKcxOhEELQ0NAAQFNTE2VlZdi2zYEHHpiV7ddsrASgbGzPm38ONN/9z29TsXozDz56L9XN67nxt7dy2GGHcOCRM3K63482f0SgBUwf3r2u8UMM0cqQYOkChyiGqOeiKcfttF6hkKplQSIQO0gPQWtqxo7rDc3itOk/ojSaqYzx/DS1iY2k/BRpP0XKT5H0GoinKqlJbOH+9a+wKdnA3D3mIJTDhqYNVKWb0NDRCGhytrMlUUXcD0hIcNsp69SAK7ZcwEHN+/Lmbx4hrTs4mkdgKjABW0OzDTTLIFJWwMFzjkXXc+uI6zfGEWYEvax/BMua117j2SeepMYyGeN6nHbssUz86lf7Zd8dUdxSSrr29QaOOU2iDcC0UD5Qt7kagJKR2RcsQggeeOABTjvtNKLRKPX19TzyyCNYWWyxEN/eAMATf1rIEzeZaJqNFS3jwlv/OODl4l0RiYbYa9YezF5/FGvXrqUmsZmPV67KuWB5e0PGkXr27tlx7B3iy0N+/6LyACU0TN1m/kH/L+vbNo0Qo4r27PD58/fv/rak9Em6jTQ79TSktrI9uZnq+Eaq41uotzfzUV0BuhHB9E0K/BDCBT0u0BsEpm9Q6mYSGVcWvsXMgw/p60frFGddpmrJHJk7K3aALZ98wjP3389GISh10pyxz95MP/vsAU/0BdjyaSYRWqrg8w50XyLqqzLfQ9nYkV28suf4vs8111zDI488whFHHMGyZcs46aSTWLlyJcOylHMy89hDSDXHSTY24SRTVK37mGT9GpCDp/P08acdwfEcwdVX/JZQuHfJ5htrN7J803Jq47VYukXEirDbsN0oChfxac2njCoaxV5j9kLTNNY3rMeWNmNL+3cadojBz5Bg6YJABTvFTnLBcccdR1VVFZqmUVBQwM0338ysWbNYs2YNc+fOpba2lqKiIu6++26mT28/jKppBrFQGbFQGaOK9tj5yW7ojzWffEh4cT3D/hHwwpv3Iy1QtkDYOnrIQAuZmGELKxIiFIkQioSJFhQQjRYQicR6dDXpbcqcpKyJuZn3r6+s5Jm7bVxUOgAAS5FJREFU72ZVOk00leL4CRM48PLL0e38yBfZXtPI0t+/j0Bj+tEjBn3bh77QuC3zf6F0dPYjLCtWrGDr1q1tDRwPOOAAxo4dy/Llyzn22GOzsg87YnPUd77Z9vjBa25hU9NWDKv/S4X7ihQ+qza+z2+u/BgNDSE0NKGjtd5qGrqmo+sGX51zJHvtuweVDZX87PGf8aH/YZfCuyQoYVpsGp8mPiWsBndvtiEGhiHB0gVSyZxfkS9ZsoTiluqUpUuXMm/ePN5//30uvPBC5s+fz7x583jooYeYN29em9FXNnlx44tc8f4VHDJybw5L70uhV4Cd1LB8A9u3CAUWEbnjlVcSSBJQRxPQgCSlOaR1F9fwcA0fzwwILIW0AEtDhHS0kI4Rshj2YTMFcgT/3vAaRQ0bKCobhV1UiF1SghkO9+n7rlm7ltsWL8bwfQ6NFXDET36C1YOEzo7E48SJE7Ftm3A4c6C9/PLLOfvss3s1RjtkgSZBamz9tL5tve/5GOaX6yfZXLcdoUUw7exXqIwbN47Kyko++eQTpk2bxtq1a1m3bh1TpuSuBDnRsB3Tyq+y6+5y2H5zqKmuwQ8CAj/AD3yCIEDKgCAICGTmfn16Ex8s/5hJe4/l/KXns53tnDf6PI6dcixjSsbgBi6NyUbW1qylIdnA7sN2Z/329Ty57kk+in9EWqT57m7fHeiPO8Qg5Mt1dOwluY6wFO9QStvY2IgQgm3btvHOO++0lZmefvrpXHzxxaxdu5Y99tijgy31jKVrlvK7t39Hwk+gCY2yQyfy9f2+1+4VfxAEJBNx4vEmUokEyUQCJ5nCTabxUy5+2kWmfZQjEY5CuGC6OkZCw/JM7MAkFFiEpQ0UEjR8Svjn1+MCNTvsRwqBb5oElok0LaRto2wbFQpBOERTUTHby4dRWFiIZduUjR7NrDPPzLxXSh6+4w6UZfGDb32LYTN6PhffkXiEjNnazJkze7zNXYkWhDn/2kNZ/N+vUr/K4pWn3uGDZ6ohbXPM/N2Ztu9uXW/kC0KicTuGlZvqtREjRnD77bdz1llnoWkaUkpuueUWxo8fzzXXXMNtt91GTU0NH374IRdffDHLly+nvLxvlSvpeAPWIHVUnXPSoV2+xncDrvntb4jGIvxr9b+o0qq4cq8rOeOAM3Z63djSsUwf+1k0+DAO47yDz8v6mIf4cjEkWPKE888/nxdffBHIeEds2rSJUaNGtU21CCEYP348GzduzIpgeXzd41z1xlUAnL3n2fx0v58StTq26NZ1nYLCIgoK+3YwDvyARLyJ5m2FXLVHKWODYVw4+Xy85ib85jhBIk4QTyCTCWQiCakUKpWCdBpRt50NBYVUahp2fT2uYRDU1zP961/HikR48trrqAqHOXHPPXslVqB98ZgLCoujTD6smHVvNLDyH00IMpGb+prmnOwvX0k3N2CFc3eCP+ecczjnnM97fSxcuJCFCxe2846+4aYaKR65e9a3my9sr2kCAYVFBYRDre0+B0++zhCDmyHBkif87W9/A2Dx4sVceuml/OY3v8nJfvzA59ynzuXjuo8BuGS/S5j3lXk52Vd76IZOYXEJBUXFrClPM6lsOpNPOr3b73/zT3/CTKW49OqrefGWW3hlWw0fPv00NRs28I7vMTEe54B2TlA9YVfxuON6pRQHHngg119/fZ+vxr92zmz8MwNee+oDtlc1s/Vdn02ra+H4Pm12UOGmmigYNmagh5E1fLeJaEluTPDyge01mTL84tJCJo+dgC51nlz7JGcecOYAj2yILwNf3my/PGXu3Lm8+OKLjB07lsrKSnw/47KrlGLjxo19bkb3fs37bWLlxTNfJFABj6x5pM/j7imVDZW4msukkkk9ep+Usm3KyjBNlCZ47IMPeKOxkeGpFGdcckmfoyJ/+9vf2LRpE9dccw2XXnopAK+88goffPAB7733HsOGDWPu3OyYbBmGzpHfnMWEqZkE5Kn7f7kqJ3ynmUhRbqvF+gsn6aBkisJ+dL3tbzbVbOaD0g9YsOI/OOTeQwi0gL2H7T3QwxriS8JQhGWAaWhoIJlMMnp0xpvl0UcfpaysjOHDh7Pvvvtyzz33MG/ePB5++GHGjh3b5+mg/Ubux77D9+W9be9x9INHt62vTlRz0czed37tKR9t+QiAKcN7lgC5o2A5bP58ZmzahHI9jGiE8IjsVtzMnTuXH/7wh9TV1bUJRdM0WbBgAXvu2XE5em/4+M2NKF3xlQO+uNMJuxK4HjJIsO2T57h3/lNYpo5p6piWiWWZGLaNZduYoTBmOIIZihAdNooJx89D5EFlleskeeK2nxL4LoZttXWTTnoreO9fzZhmDNOKYdlRrFABdiiGHS7ADhdgWuFBVx0mpeQvG/7MptgGpthTOLrkaL425WscuHt2TPiGGKIrhgRLF+Q64baxsZEzzzyTVCqFpmmUl5fz+OOPI4Rg0aJFzJs3j2uvvZbCwkLuuuuurOzztmNv4/q3r8cPfCYVT+JP7/2Jx9Y91q+CZXXNagCmj+mZ2+WOgkUIQWEfI0470pF4DIVCNDQ0tOW33HfffcyaNStr+wXYUqkTCwTXXfk60hQoUwMrs2imhrA0DFvHsHSskIEd0rFtnXDIIBo2iYYMYmGDgrBJccSkwDYI6xpaHnjOdES6ZhtG5CjGFr1HKOTiuR6e55NuSuL6Es9XeD54gcCVGq1NMuaNHEfZzDkwwJ9t48f/4tPXtxAukSgFgacwowprzEPUux64QKL99yolUH6IwLORvo2uRUCFQIURZBZNhCH6CkKOxgqOQDfC6HoY3Qhjmp/dmlYY04pi2mFMO4ptRzIizzQQWva+o1dXvs0682N+POoyfnDcuVnb7hBDdJchwTLATJgwgbfffrvd56ZMmcIbb7yR9X2GjTBXHXIVbuBy1ANHAfAfM/8j6/vpjIqGCkIyRGmsZ/P9UsqcOYh2JB6rq6s5/fTTCYIApRSTJk1qyznKBqtr6nhmRowDGwNKDBvpSnADlCsR8QDhSTRPonsK3c8sPuCTOR/WtrPNQIBrCHxT4BsCaQqkqUGL+BGWjt4igrA03kmmmFEWY2yhTThkEg7pRMMGsZBJLGxSGDEoDGeEULZEUKKyEsOewaHfOZTy/Tu/SldSsv6J21l6z+Pcff1N2NofCJsBpi7QdD2zaBqGoRGN2MSKCykoG075lBmMOfKsrIx3VxprNwLw7d/eSHHZ1Lb1UkoCzyGdbCadbsZJN+Om43hOHNdN4HtxPC+BL5NsWr+FWJnCLAxQKolUKZSIo6ghECl0rRGlNZJiPUK4CCRtf/wukL6JkiYqsFHKosVcCYHddiuEjSZCaJqNpoXQtDC6HsosRgjDiJCUkhtXP8ZqbRVl3kjOPuzknHyfQwzRFUOC5UuKH/gcveRomr1mztrzLL65+ze7flMW2ZzYTJko6/H7lFI5C6V3Jh6XL1+ek30CPPbpJj4eb7N41iTGFXft4aGkwnMD0imf5rRPU8qlOemTSPskUx6pdEA67eM4AU7ax3MDfEcSOAHSlSgngLgPngI3I4YO8RVWkCBBh0EBAAItI4SCFjHUKoKwNESLGNJbIkGmrWWiQbZOKGQQDhkZIRTSGSNrcVatA8qJjOq6i7jQNCae8D3OsCPEa7aSbGwg0diI76SRnoP0XaQf4Ps+DfXNbNraSMLdAs8vZ8G+RyMKst9or6l2K5ohiRVN2Gm9pmlodhjTDlNAF+aIPfSvk9LD91N4ThI3ncB1k3hOCs9N4LkpfD+J76Xx/RSBnsIP0gRBChmkkTKN/P/bu+/4qKr08eOfe2cyk0nvISGh16CEXqU3QTBgZ0FAUVxXXVYsgA11vyK/FQvKiqy97IqKgoAgUgUE6R1pISGF9DaTTJ97f38EokhCMsmkEM779ZqXZubec8/NJTPPnHvO86hWVMVW+l8sKBSiSDZQ7aDakFQ7kmIvDY6cLnDCDpOWUxod00Mf5d4BdxHg4+fmb0oQPEMELNepbEs2RrsRL9mL5/s+X+fHz3HkEO0d7fZ+tRmw1JedBSYiVLlKwQqAJEvovLXovLUEAJ5aY6MoKlbrxSDIbMdkcVJicVJicWC2OrFanVitLuw2F3abE4fNhcum4LKXBkJYXGB0oDgUXA4VHAqqU0VxgQUo+MOxjgAQjkayY4isPGABkLU6mo+cUuXz2f3v2ezbcQjJp3Ym9RYX5OLlq6DV1l3WVlkund+j0wXgW8vF151OB3ZbCa7E/7L84HsERecS2kgmSAvXJhGwVEJBqfC1YnsxezL2EBMQQ7tgz07CrG3fnv4WgMe7P17nx1ZVlQIK6O3nfqVkRVFqvThjXTuBhu7ausllYbPZeOKJJ1i/fj3e3t7Ex8fzxRdfeDST7585XQomiwOj2YnJ4uDjvds5bdOxONoPQ1gUci3d4jPn5+KjV0FTO+0XFxSg96/9f4vlXZuEhATuueceTpw4gcFgICIigiVLlngsqSSAVuuFVhvE8BsehoPv8frRZUzr9qzH2hcEd4mApRKqqpa7THbeznmsOLOiLGmSTtYxpuUY5vWbh1Zu+L/WZGMyAHe1r537+1dzoeBCtZY0Q+MbYTmZlUOh3ocBQXVTe2bOnDlIksTp06eRJInMzMyy1zyVyffPtBqZYD89wX6ltZyKfzOj87HTfEjtJpyxmIwYrRr+9+BoDHoNXnovvHQ6vPR6vPQGdAYfvLy90V5chaTz9iX8hj4EtK7ahGpLUQmGgOoVC3TXn6+N1WplxowZjB49GkmSWLx4MQ888ABbt271+LFlWWZKy958lrSbYxlbuCFqSOU7CUItaPifrA3QwxsfZkf6DmL8YpjccTLnjefZlLqJlYkr2Zu1l1UJq9BpPV8bxZNSjCloZS16Td0XBCxb0hzpfk0XVVUb1QjLD8lpgMTY1p5b7VSRkpISPvzwQ9LS0sqC8CZNPF8luTLpqp6Wsq3Wj9N1/GR8N3+P1WzBYrFhsToxmkw4HEXYnSoOBRwuGaf6+7+nqMDV/OU/a6/S6u+sJjtBtVC0sSq8vb0ZM2ZM2c99+vRh4cKFtXa8GyN6QtJuzheeEAGLUG9EwFIJVVXLljYrisLDmx5m54WddIvoxqejPy3b7pk+z/Dq7lf538n/kfB9At8nfO/xoEVRFF7f/zprk9Zid9mJ9Y/lmd7P0Dm8s9ttZZmzCKhikba///3vrFq1ivPnz3Pw4EG6dOmC1Wqt9pD0mZwzAHSKdm9J8yWNaYRlR4GJCLTEBNTyhAQgMTGRkJAQ5s+fz8aNGzEYDLz44osMGzYM8Hwm34pckP25ycteK23/UVS/BKL6VbKiRVVRHTYc5iJWPzMNyY3bRzaTUmdZbSu7NosWLSIhofZW77QJ7QKATq6bESVBKE/jeeevJSoqRruREd+MoOvnXdl5YSfdI7vz8agrc6LM7T2XSR0mkVacxu2rb0dRKp7/4i670864leP47MRnWJwWvLXenMg7waS1k1ifvN7t9ox2I1G+VZvseMcdd7Bjxw6aN798NcSMGTM4deoUhw8fJiEhgQceeKBK7SUVJmFQDAT7uj+BrzGNsKiqygm8iK+jrw1Op5Pz588TFxfHvn37ePvtt7n77rvJysqqtUy+V/bBQaZXEE29635kr1yShKTzRhcUidkG/gFVWwFjLTGiOGT8QytZBeQBlV2b+fPnc/bsWV599dVa60OwT+kE+UJreYvoBaFuiBGWShgurgAosBUQFxbHmJZjuDeu4qqjc3rPweKy8N2Z7xjw1QDaBbcj0jeSaXHT6PCHXA0VWbB7AZtSNhGoD+TeuHsZFDOI3Zm7+dfef5FtzubOdnfyQt8XAEg1pTLh+wnM3jabATED8NH6VOmcjHYjLtVFq6CqzSEZOHDgFc/VZEg63ZxOiFy9b6aNKWA5lpVDkd7AwOC6uX3YrFkzZFlm0qTSpF9du3alZcuWHD16lOHDhwO1l8n3kqz8dBRJQ7RfUK20XxNGq0rbkKottS/MSQQgILT26yBdLcvywoUL+e6779i4cSM+PlX7+6+OIxlbS/sSdG0tLhAaFxGwVOK/t/yXNFMacaFxVd7npX4vEagL5JvT37A/az8qKj+c+4EhMUP416B/4a29clg1z5LHvevuJdWUio/Wh2xLNs/98ns1WQmJe+Pu5emeT5c9F+sfy6QOk/jo+EckFiRyY3jVanrsztgNQOcw928lVcSdIelsezaxhthqH6u2EsfVtR+S0kCtm/krAGFhYQwbNoz169czZswYkpKSSEpKomPHjrWeyfeS9IIsQE90oPs5eEaOHElmZiayLOPv78/bb79N165d+fHHH3nuueew2+34+PiwdOlS4t2s1u0oMWJ1agmIiKzS9gXZpQFLcERLt8/DHSUlJTgcjnKvzRtvvMGXX37Jxo0bL6syXhP5JekUWDJpERKP5uLiga2J/+X/9rxOiFame8wtHjmOIFRH43jnr0UBugC3gpVLZvWYxawes4DSkZCZm2eyJW0LNy27iZHNR9IhpANmh5ndmbs5W3iWQlshAHe0vYN5/eZhd9r57MRnXCi5QMvAliS0SSh3zkmET+mQdKY5kxupWsByIPsAAH2j+rp9XuW5NCS9adOmSrdVVZVCCunrX/1jN5aAZVdRCU1UmSj/ukvE9d577zF9+nRmz56NLMssXboUm83G2LFjay2T7x9dMBUATYgJdb/I49dff132wbxixQqmTZvG1q1bmTRpEtu2baNTp05s376dSZMmcezYMbfaNp0vLQjq36R5JVuWKspNAyAownPLiMtTUZbltLQ0nnjiCVq1asWQIaWTYPV6Pbt37672sVYee4sX9n+AikSwVqKNXyjZViPnrXaa6XUsGvYeWk3DXkwgNG6N452/gYv1j+W7hO9YcWYFr+17jdXnVrP63GqgdOQkSB9EzyY9mX7DdPo37Q+ATqvjgc6Vzwm5dJvpXOE5qNp7LafyTyEh0TywijtchbtD0ul56dg1dloHu1/k79KcoMZwS0hRFE5IXvTTem6eU1W0atWKLVu2XPF8bWby/aP0khL8nSX4V+OW0B9HEYqKipAkicTEREJDQ+nUqXQC94ABA0hJSeHAgQN069atym2b0s8C4B/Ttmrb52Uh6xR8/Ko2IlNdrVq1qvDaqKpnc/eczD2CisT8Hg+yI20L54uziPEJYXrczdzaaWbZiIsg1BfxL7AOTWg7gQltJ5BVkkVSURKyLNMjokeNVr3Eh5cOfS8+tJhJHSfhp6v823qaKQ1fL99qH/OS6gxJH79Q/SXNDocDuHZHWFRVJSO3kNMpGexPz8DkH8pvTid/23EKX63m4kPGX6vBX6flluahBOnrJj9LXUm3OYh2FVV7/ylTppQFXGvXrqVZs2bk5eWxc+dO+vXrx6pVqzCZTCQnJ7sVsBgzzgPg16xjlbYvzs9F70u5OZquVU0DmiGxh0i/5vy/USvquzuCcIVr853/GhfpG0mkr2e+mWllLRpJg0t10ffLvjT1a4qP1ocgfRChhlCi/aIZ03IM7UN+DxDyrflVXiEE8NBDD/HDDz+QmZnJqFGj8Pf3Z+vWrdUakj6VfQqAG5re4Pa5FhcXA5Rl/GyILHYnW46e51RKBlp7CfkFBZhNhSjWYvQuM9qLmZOLvH0wdBlIipeeFJsZHFd+8L149gJHRsRj8Lr2R5QuueCSaYq52vtfulX16aefMnv2bNauXcvy5cuZO3cuxcXF9O3bl7i4OLeDWlNOJr5aB1rfoCptby40oQ9oXMHkXZ2f5svTq3hu54u85xtLq7CqB3yCUBfc+qtesmQJS5YsITk5GYBOnTrxwgsvMHr0aKA0z8OTTz7Jjh07sNls3HzzzbzzzjtERl79w/nf//43r732GpmZmcTHx/POO+/Qq9fVq7cKvzs05RBrzq3hgyMfkGnOJMecQ6KSWFZW4KNjH6GVtdwQdgPP9HoGm8tGi4AWVW5/6dKl5T5fnSHppKIkfBQfAgxVywHzR/n5+QAEBLi/rydZ7U5OnM/kTEom6Vk55OfnYykuAlsxBsWCVir9vSgq2GRvVJ0v3oHhBAYHExkWRrPoCNo3a8IbQb5IkoSqqpgdCoVWB0UOJ0V2F/fvO0uBn4YOPx3k5dbRTO3gft2lhigNA3jVfDXL1KlT+etf/0peXh5DhgwpC5ptNhtNmjQhLs69eWeFOWmUOL1YveQR9D6+6H398PYNwNs3CINfEAa/UHwCwvH1j0BnCMJiNOMTXPNRyuqyO60U2/IINER67FaNXuvDgoH/4tEts7hz7VSmtRnMw71fF/NWhAbDrX/pMTExLFiwgLZt26KqKp9++ikJCQkcPHiQFi1aMHLkSOLj49m8eTMAzz//POPGjePXX3+t8LbHV199xaxZs3jvvffo3bs3b731FqNGjeLUqVNERNR+joPGYmyrsYxtNfay5xRF4bf831h9bjW/pP/CoexD3LWmNBV/oD6wPrpJujmdMLl62UELCwuB0sDY4XCg1WqRZRlJksoel6iqellA9cf/v7SdJEkoqkqBTcbHPwCdToveS4veywudVoPVauZ8ejapWbnk5OZTbCpCtRajV6xoyoISCafGG723Pz6RTQkNCcHHP4g2sZH0bBeLTlf5t3BJkvDVafDVacoKGZ4Y243Fx9N4MyWb2RnZfJ2ez7u9WtPcv+GOLlXFaV0TRsh5bu9XWFiI2WwmOro0cFu5ciWhoaGEhISQkZFB1MWKz//85z8ZOnSo2zV1QnVpBBkCSN6fhMsGLrsEVHC7R1JBhaDYuqn/dImiKLz5y8OsPL+LQlfpscO0MjPjpzH+Bs/UBOscPZxVt/3E/G1/5f0zW9iQ1o/FIz6hWbD7I6KC4GmSWsOZWyEhIbz22mvExsYyevRoCgoKyr4BFxUVERwczE8//VSW5+HPevfuTc+ePVm8eDFQ+kcZGxvLY489xpw5c6rUB6PRSGBgIEVFRfX+7bshSzOl8eBPD5JWnIa3xpsNd2wgyDuoTvsw+KPBtPFtwwd3f+D2vocOHWLlypWe71QlHKqMVTYge/vi6x9IeFgozaMjad88ilbR4bU6CdjqdDH8p6Pk4eL5EzaCfHVoo3wJah7IDR3C8TFcQ7clFBd9f1zHqAAvXrzJvTpC58+f584778RisSDLMuHh4SxcuJAuXbrw4IMPsn37dpxOJ3379uWdd95xf5nvi4GYYlrj/8CBi111YinJw2zKwWLKxVych7W4EEtxETazkQtpPxDWTiW8aRwajQ+yxhet1q/0ofFDqw3EyysArVcQWm0gOq9gvLyC0Wh8qj3vZcXR13nhwCcMDY+la0Q39Fpv1iat57CxgFd6zGBcp79Xq92KbD+3jLk759PCN4AvJuzwaNuCcIk7n9/VHkt0uVx88803lJSU0LdvXxITE5EkCb3+9wyW3t7eyLLMjh07yg1Y7HY7+/fvZ+7cuWXPybLM8OHD2bVrV4XHttls2Gy/1yIxGo3VPY3rSox/DOtuX8f7R97n7YNvM239NFYmrKyz4ztdTgqlQpr5u593JC8vj2nTpuFyuVAUBYvFQkpKCrt27SIgIOCK21N//FD48+jepW1VVeWr9dsx52dxy4Q7sTmcOB0u7E4ndqcT2cub5lHhtG8aip93/QQG3loNO8Z04YcNZ4jPzcSVZ0Fz3gK/5rI/IJnuT/TCR39tTEVTinNI10cQY7C4vW/z5s3Zs2dPua+9//77Ne0aAL7piWX/L2u0+AZE4htQ/u3sbdt/wOEwkl+wE6jOdz4ZSZKRJM3Fhxey7IUk6ZBlHVqtH53i3sTX9/fkjv899TVtfQwsGvN7raO7Os9hyspBLDryEbd0fNSjZSsGtLqHOy9s54PEbRTb8vDTu587RxA8ye13uqNHj9K3b1+sVit+fn6sWLGCuLg4wsPD8fX1Zfbs2cyfPx9VVZkzZw4ul4uMjIxy28rNzcXlcl0xxyUyMpKTJ09W2IdXX32Vl156yd2uCxc92PlBTuSdYGPKRn698Ct9ovvUyXGTcpJwyS7ahlZt6egfhYaGcujQobKfFy5cyM8//0zPnj1r1Cfbul9waX0YEN+wM3hGX7wV5JzcgdAWgez5z0HaZdk5/89dpPeJYPhY91dd1bWcokzsso5mfg1rErHLUYIGyO3Uk6rehB44YO9lPyuKE4fTiNNRgMNRgMNRhMNRiNNpwukqwuUsweksxukqweUy43KZUVwWXIoVRbFdfNhRlGIcDjsWi5XMrJW0bjWL3OIU3tk9m1MlZuZ1vTzLtkbWMrHDRObuXcr4b3phVVyYXS6ivH24u91t3NH5aarrUPpPfH5uGy299SJYERoEtwOW9u3bc+jQIYqKili+fDlTp07l559/Ji4ujm+++YaHH36Yt99+G1mWmThxIt26dfN4sbq5c+cya9assp+NRiOxsdXPnHo9mtNrDhtTNrL63Oo6C1guLWnu2KRqS0ev5sMPP/RI7RSH1YxG3/DnhchepSNGqlPBz1fH4Jk9ObE/g4KfU+mwI5u8vrGEhtZeanZPSC0qAIKJCaifCscVsecfxQAY/jQHzB2yrEWvC0Gvq3kxxKzsdRw79ihJphz+vW4cP+cko6Jyd7Nu3HbDk1dsf3P7GfyStpUcaz5h+iD8dH4cyTvNSwc/x2Qr5L6e86vVj2XH3sMgS3x265qanpIgeITbAYtOpyub0Na9e3f27t3LokWLWLp0KSNHjiQxMZHc3Fy0Wi1BQUE0adKEVq3Kr1kTFhaGRqMhKyvrsuezsrKuWvZer9dfdutJcF+kbyReshe/5f1WZ8c8nXsaSZXoEF15TaWr2blzJwUFBYwdW/0PmEtUuxV9SO0m//IESVMa9CvO0pVfsixzQ8+mrE0qgNwC7B5OIlYb0opNQDAxwQ1rMr099zgGQBvifkbr2mCxnGebScuK1NUEamTGx3RhRo8XiQwoP9miVqPj1ZHLL3tOURSeXJ/AWydWERPQmhHtp7vVB0VROFJwnjZ+IQQZKn4vFoS6VOOb34qiXDafBEoDEYDNmzeTnZ3NrbfeWu6+Op2O7t27s2nTJsaPH1/W3qZNm3j00Udr2jWhEjH+MZwtPEu2ObssxX9tSi5KJkAJwNurZiXqP/zwQ6ZMmeKRBHJaxYqvX92lxq8ujbb0NorLdXlmXNlZGqjkLD7EoQ4BqLKErJFAlkEjIckSkkZCliUkjVz6/xoZ+eJzpf8t/VkjS+hsLryKHKixfsg6Ga1WRqvVoNdr8dJKGPRavHWaao2apltt+DtLCPBuWCNaroLTAOjD3as/VFtS8g6ztsiLfsERLBqzBn0Vi5r+kSzLLBjxDVO/H8pLexfRI3YMwT5Vz72UYTxFqs3O5HbD3D62INQWt97x586dy+jRo2nWrBkmk4n//e9/bN26lfXr1wPw8ccf07FjR8LDw9m1axczZ87k8ccfp3373++vDxs2jAkTJpQFJLNmzWLq1Kn06NGDXr168dZbb1FSUsJ9993nwdMUyvPPfv9k8rrJTF03ldUTVqOt5dTbFywXCNeE16iN4uJivv76a/bu3Vv5xpUw2+zocRIU2PBXll2KDxTX5SMpNw5pTmpyMVqnQsRpIxoFNKqKRgFZVdEqoFHBq4YDMM6Lj0vTZS0asGkk7BoJh0bCqS19uLQyLq2EopVRvUof6GTw0uAsCuEvShHmVauQ9DokgzeSwRvZ3x9tu45IHr51XFVqwTlcGglNHQTtlbHYTbx2ejfessqLg5dWK1i5RKf15tUh73L7mnt5fst9LL7lxwq3PZ39K2fy9tMpsj8tQroQ7NMUPxm+OruKYW3+UuHojiDUJbc+obKzs5kyZQoZGRkEBgbSuXNn1q9fz4gRIwA4deoUc+fOJT8/nxYtWvDss8/y+OOX5we4dMvokrvvvpucnBxeeOEFMjMz6dKlCz/++GOlyeaEmouPiGdi+4l8eepLRi4fybKxy2p1pCXHlUO8b82+xX711VfEx8fToUPNbisBpGQWABAWdA0ELJduCSmXRx5NowJo+kzlhSRVVUVxqThdSunDqeBwqrhcCg5FweVScSoKLosTtcCGPUiPcnE7xalgMdmQNDIuhwuXXcFld6E4FFSHC9WhgENBuvjQOBW8zE60DgUvl4qXU0XvVGnj0mFw6cg/+8eeOYB8QodvxDB8pOd+YW6QitKx+fji0wDS7L+96++k2uwsHfQyTQLcn5z+Zy1CuvD3uPEsPL6ST/c9z9Qe/7ximwVb7+e/50u/AEgsYVx0R+YN+Zw3BrzEEzvmMXHNbbw1+HU6R5efmkIQ6kqN87A0BCIPS80s2r+ID459gJ+XH2smrCHU4PkVATaHjZ7/7cnkqMk8Par6Kxf69evHgw8+6JERuE37fmP7mq8Ydcdk+t5Qu1V3a+rs2Ty8PzhB/phmdB5Y86KV9UV1KahWM2pJCWpJMY6kZPJ+0hE22o73oPq5/VC0pC2SrCPgoeP1cvxLzHYjty4fQLuAKN4dW/FoSHVMXnETaWYTWycdvuz5f++cyXtnNjOhaSemdXma7357j8/P7aS5tzf3d5pEpF9zZvw8jyidlp8m1k2BTOH6Uid5WITGY2b3mUT5RfHPX//JmO/GsOHODQToPBv4/ZbxG6qkVmtJ8x/t3LnTQz2CzLxCAGIjar6y44/OnDnD1KlTyc3NJTAwkE8++aSsmnB1SRWMsFxrJI2M5OsHvn5AJM6sXMCOJtyz18AdXsVG7DE1uz415XI5+MeP48l3upjW+RGPtr3y2Jv8Ziqipc+VpQTWpe6gV1AYLw9fBsCTA/5Dr+gvWbjvdZ7f/1HZdjdF1u/vRxBABCzCRXe1vwu9Rs9zvzzHxDUTWT1+tUeXo5/IOAFAp+iG88aXX1iEokJ0WJBH233ooYeYMWMG06ZNY/ny5UybNq3Gc260movLmq/xgOXPXLmFgA/aqKpPCPUkVXGht9iwBbmfzNBT7E4zL226m10FObzQ7T56NRvnsbYfXTOKn/Mu0D0whMWjv7vidbPLSbh38GXPDWw9kZta3s2Z3F85mrmDSL9m9G9xl8f6JAjVVT+z3IQGKaFNAne0vYMUUwrTf3JvGWRlEvMT0SgaWkWUv8S9PhQZTdglnUcDs+zsbPbt28fkyZMBuP3220lNTeXs2bOV7Hl1l/r450m31zpXgRmZIqTA+pnwai88hUZR0YRUnDjQarUyfvx42rVrR3x8PCNGjCi7nqqq8uKLL9KuXTtuvPHGsiKMVeVSnAz+sherMpN5oO0I7rzxiRqdzx+tPr6Yn/MuAPDBuA1XJH9bdmg+OQ6FPk0HXrGvLMu0j+jHHZ2fZkCrezyeS0sQqkOMsAiXmddvHueN59mbtZf9WfvpHtndI+2mmFIIVAM9shTZU8wlJShaz+bzSU1NJSoqquw8JUmiWbNmpKSkuF2Q749krYTKtX9L6M9cRXY02pLfl0HVMUfOQfSAJvzqxf1mzJjB6NGjkSSJxYsX88ADD7B161befvttjhw5wrFjx9DpdGRmZrp1/JNZv2BSJO5v2Y2Z/d6owZlcKaskFYC7m3VFUX9fDn8sYwvvH3yNLTkp9Atpwq1xnq1BJAi1peF8eggNxsxuM5m8bjJ7M/Z6LGDJtGYSrq3ZkmZPc1hL0OoadnbYSzQaGSe1d0soLy+PYcN+n/RqNps5d+4c2dnZPPHEE/zyyy8YDAb8/Px46623alwS4RJXkQONwe6RtqrDmVt6q1If0a3Cbby9vRkzZkzZz3369GHhwoUAvPbaa2zevBmdTgdw1YSX5bE6SwBoEdzZrf2qYlr3f5JtzuKr8/vY+nUfboqM46wxhcPGIgI0EtNbD+LhPq+L0RPhmiH+pQpXsCmliQC9tTVL8PZHea48ogz1M0+hIqrDit7g2YAlNjaWjIwMnE5n6TFUlZSUFJo1q9kcCVm+OIellm4JXarVdOlxaUQhJCSECRMmcOLECQ4fPszcuXO58847PXZcp1mHNlDnsfbcpeYn4tDKaP1iqrzPokWLSEhIwGg0kpWVxffff0/v3r3p3bs3X331lVvH79r0ZmL1OtYne3ZVEJRmwH1m8Cf8Z/A/aesfwcaMYxTYLDzZaTyb7v6FmTf9G50H/8YFobaJERbhChZHaXowTwUsVrsVk2SiWWD9TWwsj8ZlxcfDWW4jIiLo1q0bX3zxBdOmTePbb78lJiamRreDADTa0u8WdTXp9o+1mv6YqbpPnz6kp6fjdDo9c3tPkrBm6ShY9CmSl4ys1yDpvZC8vZC9LyaX8/FGNvgg+fri0GnRBAfi5ReILF9eRNFlzaNo/UNIshbJEIzkHYpsCEPjE4Hs2wStb1M0fk2RdL5wKedKURp2g4Gq1uKeP38+Z8+eZdOmTVitVpxOJxaLhd27d5OcnEy/fv3o0KED8fFVyzckyzIGjRan6nLjl+ae3s0n0Lv5hFprXxDqighYhCvIUumHo0vxzJvoqYxTIEGb0IaT68Rid6BXHQTVQt6epUuXMm3aNObPn09AQAAff/xxjdvU1OEqoavValq0aBFjxozx2Fwk3w4uLIku7Hl6VJcXiuKFqupR8eb3AWAXYAJMKKrC2rT/UOIswkt2odeo6LSg85LRKcWE64wMbJKEpEJFaeBcsoRTp8Wp0+NnMVMSVrWRv4ULF/Ldd9+xceNGfHx88PHxwc/Pr2yCdYsWLejfvz979+6tcsBithtJspQwMKpHlbYXhOuZCFiEK0T6lGYZzjZne6S937JKCyx2jKp5lWZPSckqQJYgNDjQ4223b9+eXbt2ebRNTR3mYamoVtMXX3zB119/zbZt2zx2LP/J9+BfzvOq04FqNqEWm1CLi1HMJVz4NQ1tcjj9B8eDbMRuLsFmMWO3WLFZbVzIUUkv9GXg4n0ggcOShbM4HZc5A5c5G8WcjWLOgZJcsOQjmwtxWE1IXSZV2s833niDL7/8ko0bNxIUFFT2/MSJE/nxxx/529/+Rn5+Pnv27OGpp56q0rmb7UYeW5eAS4VRbf9Sxd+YIFy/RMAiXKFNUBtkSWZTyiYe7/F45TtU4mzeWTSKhjaRDWeEJT0nH4CosOBKtmwYtJq6uSVUUa2mr776ipdeeolNmzbVSdkMSeuFFBACAb8nlLMeKMRHVeh03xPIWs0V+3z31oNYTqYja0rf1rx8m+Ll27TGfUlLS+OJJ56gVatWZcuW9Xo9u3fv5tVXX+W+++7j3XffBWD27Nn06tXrqu2lF55i9cn/sCJpMzkOB/O6TaNDZP8a91MQGjsRsAhXkGWZobFD2ZiykdnbZvPKTa/UqDBiivHikmZNw/nnlpVbBEBs5LURsJQVP6zlAZbyajV9/fXXPPfcc2zcuLHGk4drwlFowaYq5QYrAMV5hRiCPLtMHSAmJoaKKpiEhoayatWqKrWz9rcl/OvAe+Q5FWRUOvkHsmDgC3RtOsqT3RWERqvhfIIIDcr/G/D/uGvNXaxNWsvG8xuJ8ouiqV9T2ga3pW9UX3pG9kSnrdrqjgxrBmHasFrusXtyC4tQVYgKDarvrlSJ5uIkUYXajVg+/PBDHnzwwcuemzRpEk2aNCEhIaHsuU2bNhEa6vmaU1ejmlw4NBUvgTYXWghpVn8p/ityPv8I87Y9xv6ifOL8/Hi49a0Mbn03kf4NJ4miIFwLRMAilEun1bFy/Eq+OPEFn534jKySLM4bz7Pzwk4+Pf4pAAaNgUjfSPpG9WXqDVNp6lf+8HuukktP//LzdiTlJXEg5QBJKUkE6gMZ1WMUzcJq/1u80WjELnk1qER2V6OR62bSbXm1mhwOR60es6pkq4TirVT4urVIISCsYQXGNqeZGeunUuJy8Wz8RO7qPEfkPRGEaro23q2FejM5bjKT40pXQSiKwrmic2xP386BrAMkFiVyoeQCX576ki9PfQnA4JjB3N72dgbGDESWZUpsJRRLxbQIbIHdYWdv0l52JO3gWN4xztnOYZSNlx3v7TVvE6eN46FuDzE0bmitnVdJLWS5rU3SpRGWxpXo1i16lze2AGe5r5lNObhsMpmJJ9m87AUMfoEY/IIw+AVj8AvBJyAcn4AmePsE12nAsDNpORfsTt4d8CwDWt1TZ8cVhMZIBCxClcmyTJvgNrQJbsN9N9xX9vyRnCMs2LOAo7lH2Zq2la1pWwHQSBfnGkiwPHk5n3/xOU7ZiaRKhKvhdDR0pFeTXvRp3ocQ/xDSC9PZcnYLyzKWMXPvTOQ9MoP8B9EioAVtwtrQKboTLcNbeuQDx24146Uz1LidulbRXIrGzmVz4C354gop/5aQRqvDrwkUpakcStyPqlSwqFlS0egUbhjZheGTX63FHpcK9C7N7rw7fTO9mt2KXnttZFYWhIZIBCxCjXUO78z/bvkfAHmWPFaeXcmezD2Y7CZK7CWYS8w08W1CC/8W9IrtxaB2gwjwuTL/SUxYDL3b9OYRyyPM+HYG+c589hbtZYtpC1wAjoC3y5suvl2Y2W8mN8Revf7L1ah2C/qghjff4VrjcDg4k3yCk6nH0chahvUYjZ9feQuVayZl/T68JAlDZPnL0PWGQB5atAYoHQl02IyUGLMxG7Mxm3KxFBdgKS7EWmLk8JpdFGa5V/Onuro0HcWw8MV8em4XP6b1441BC+kcPbxOji0IjY0IWASPCjWEMv3G6Uy/sfrVnv0N/nw5+cuyn802M79d+I2TWSfZmbqTvaa93LvxXhb2WsiwTsOu0lLFNC4rPr6ezXJbJ6SK0qHVnZzcTFbu+oadubs4zmkssq3stVeS/sWH/f7Dje0rrs1TVcdeWYlUDKpWRWPX4CUFEzOkS6X7ybKM3hCE3hBESOSVVZgPrRqDt58fDrul1kfZZFnmrTE/cCB1HXN2PMOcbU+z5q59Yh6LIFSD+KsRGjwfvQ/dW3ZnUp9J/PvOf/PjXT8SQgjz98yvVnsWuxO96iCwFrLc1rb6ClcURWHf0Z3M+vxvjFxzM+/mfoQsyUwNmch/Oi/ml9u281mPD2iihDNnx9waH89lcxBoLB0BUwFFo1AYVoBGX9Uk+hW067Tjckqc2pzE2/feyZHtn9a4r1XRLXY009rfRqrNwY+nltbJMQWhsREjLMI1J8QvhDta3sG7598lKTuJlhEt3do/LacQjaQSGiwClj+yO2wsWfMWuwv2kqvm442eKG0kEbpwTlhOclqTTJQznIcj7ufOAX8hOOjyFTldO/Xmgdz7eTb5ZXLysggPrX6COVNqFpIk4TcomuY3Xz0Rmzs0Wh13vjiblBM72L1sJzazyWNtV8bmsgIwe8+7jOn4cJ0dVxAaCzHCIlyTbu18K7IqM3PdTPYl7XNr3wu5hQBEhAR5vmO1SEGt1TtC876ewydFXxKmDWGgXz/ifW7Apbo4Yj5OsBzEG+0WsG7aBmaM+/sVwcolecU5aBUtAX41K3lQnJoLgE+M55cpN2s/kJgOfQAIDI/1ePsVuTt+dp0dSxAaIzHCIlyTmoY0ZU7HObx1/C3u23Yfflv8iPaKpmtYV6b0nHLVXC65+YUANKlB0rgLxnx2pZ7kVO559BodnSNbM6TVjWg15WdhralLq4NqK15Zs+1b1jg3MifqH0waVf35R0fzj9NabYZeX7NK35aMIgzIBLSonTIAhTnJAARH1l3yNh9dAH/vMJq3T64j03iGJgFt6+zYgtAYiIBFuGZN7D2RhC4J/Hf3fzmSfYTE4kS+yfqGFatX8NHQj4hvXn7F3IKi0twvTSOqnpb/UEYya07tZE/GPlLNx3BqMy7fIBHkbWHcFJnAv0Y8jK/eszleLuVfkTw8xGIqLmLZls94N+9Dhsh9mTjivsp3uoqj9hMM8O1T43458kqQXDJ6P98at1UeY+4FQCUovG7rWxU7TGhQ8dLULKAThOuRCFiEa5qP3ocHB/6eSj4lN4U7V93J27ve5sPmH5a7j9Fowq5q8PUuP6hQFIVPD25i+anVlDiM5DsTUbWFAMjOcJp6d6J75F/o1TSOLlGtsbscbD53mK9PruDn3I+56Yt1LBr6BgNbdvLYeXoy/4rVauHQyb38eHIt6yybsEp2xuqGM+/O+TVavaIoCnlyIUH6qgeCuSeTcZZYkPVeyHotGr0XGm8driwrtgpqBnmCKS8HkNiz7m30Bj90Bn/0vv7oDYF4+wSVPnxD0HkHeGxFj6IorDq/k74h0YT61t2tKEFoLETAIjQqzcKa0c2vGweLD6IoSrkfNiXmEuxy+cFKTrGRhOUPYZKOITvD0UsBtPXrT5/oHtzaoR/tw6PL3a91aBMe7DmKtaf288yO2fxt62SCfo4jxr8pHcJa0iKgGZ0jW9ExvAUGL/eX0l4aYanJR+e586d5afMLHOUUDtlJoMufOwJuZWL/KcRENa9Byxf7JssM0fbjk8Iv6bKvGwN7XD3fiCk9B8vHKeWOGgUTjrGDuVr9+Pjjj7n//vtZsWIF48eP57777mP//v3IsoyXlxcLFizANygESOTX/22vpDUV2UtF0oAhUOYvLy3FP7h6FaBVFPKdLtoGiRpCglAdImARGp0RrUew49gOfj71M0M6DrnidbvFjFpBWv7H1r2GUT3F9LYvM7Nvgtvfrse07073pt/y7Kb3OJF/iGP5Bzhm3IAk/55SXnIF4i2FE+wVTavAttzcui+3dux11ds9Kmpp2cNq3hE6euoAD//yKH6qD480eYCerfoQ1zbe47WU5t/1Og/+bxovHf4nazr1xWCo+JZOSXoukiRh7azgEx2C4nCi2F2odheqBO3G9Hb7+MnJybz//vv06fP7bak333yToKAgAA4ePMiwYcPIzc1l8J0yDnsJ1pJCrOb80v9airCVGLFbTNgsJditxTgsZlKOJFKQYqHEmFPtgEUja4kPCGFbxn5mVasFQbi+iYBFaHTGdB7Da4df49ldz9LzRE/ahbQjKiCKqMAo4qLjcNhtaCqoNH3KuIdY73483n9CtY8f6RfIBwmlK0LsToUsk4Vz+Rkcyz5HclEK6cVpZFkukGc/T3ruTnbkf8hLvzbl1uZ/YfaAuzHorgymLt0RkqoZsbyxcyEBqi+fTfgvYSER1T63yuj13jw78Dnu2DGRbQc2Mar/rRVuayssQQOE9mhFcLua3yJRFIUHHniAd955hyeeeKLs+UvBCkBRUdFl+3jpfPHS+VYahOxas5Cdn28lKKxmI1H9onqw5NRPnMreSfuIfjVqSxCuNyJgERodby9vFg1axMKdC/m16Fc2F28ue02jaGhpaImvqxNmhw0fr9+Dg5M5aTi1F+gROdFjfdFpZWKDfYkNbsOg1ldO8LQ4bHy0fyP/O/kF36a9xorPP+au1jN4ZtA9l424lAUs1YhXklLOcEA6zlMxj9VqsHJJ25ZxsAM2Jm24asCC3QWArajEI8d944036N+/P927d7/itTlz5vDNN99QUFDAt99+6/bIWUlhHrJWwdu36vNzyvOX+Ll8mbiJt3a/yJJxP9WoLUG43oiARWiUerXuxdetvwagyFxEal4qaYVprD65mj3qHqyas/T+/CeidT0Y2WI4Bi8dX5z4AlQDD/a4pc76afDS80ifW3ikzy38cHI/C3a/ybLz8yn6ycS/Rs0o2065GLGoqsLuw9tIyTlPp+ad6dD6xqt++ObkZTFzw98JJ4TxA+qmWvDv/bl6dBU9sDPJ27ZiW5tLk54danTMY8eO8e2337Jt27ZyX1+wYAELFixg48aNPP300/zyyy/odOWPspXHXFSAlwfqFgYYwhkWdQNr04/gdNnRaqreB0G43omARWj0An0CCfQJ5IbYG7j5xptRFIW1pw/w5fE1HC/8hU8SSydeSmog97WbQ7Og8Hrp5y0dunNLhy8Y/NkUNqSuAX4PWFTg54A9fFK0ipxDBaVPpkPktjAG+vahV0wfHIqDLGMmxfZiJEnCZDfxQ8kGJEni40Ef4OdTd7WTWjlj0XldPY2+V4ABZ1uJgLMh5BxJJLxz62ofb/v27SQnJ9O2bWluk8zMTGbMmEFGRgYPP/x7Vtnhw4fz6KOPcvTo0XJHYipiNhaj8/XMqqXBzcfyTeoR9qauoW+L2zzSpiBcD0TAIlx3ZFlmbIcejO3QA4DdKWcA6BnTukEUpevVpD/rMhaz/fxhBjSPR1EU7lg5ifSmJ+jl7M6rXabTqW1X9h77hc2JG9lk3s43iaWVivWKF76qDwoKMjKjfIbwyIjHa5Qmvzpa6ppxwnqq0u3a/GUw517ahOnLo4R2aomsqd7v/+GHH74sMBk8eDD/+Mc/uOWWWzh79ixt2pTejtuzZw/Z2dm0auXeSh2ryYzezzO5dewuCwB6Te0WXhSExkYELMJ1r3ezhpVx9JGet7H+2+/42+b7ucF/LGnmUxSqJwB4Z8rSsnk3Q3qNYkivUbhcLnLzs9B56QgMCGkQQdfNzW7mqeTnOXByN906VLzax8vXG93AUHx3ODj7v620u3eoR/vhcDiYOnUqRUVFaLVafH19Wb58OcHB7s1FsRXbCW4actVtim15rDrxLkmFZ1iecoARTdoy6Ya/0TFyADqtN4qisD/tB94+9C6BGon4piNqcmqCcN2RVE9mpKonRqORwMBAioqKCLgGK/AKwp8l5eXz8Lp5pNv3Iql67m37OE8OSPB4ptva4rQ7GP/5WLSSliXj/kNU+OWrcEpS87iw5Sj2s0X42QLQSFoKQvO58amEeurx1b0zfQzBzfzpOmo8Wp0BLy9vvPQ+eOl80Op8OFN8lNl7X6HAqRKgkTC6fn9blVHxliWsioqCRJiXzCt9n6dfyzvq8YwEoWFw5/NbBCyCINSKI7/t49FdMynQGLkleBQzox/E9GsKpDvxUf1wqS6MUh5Sc28iB3YkvFPDTaj274fGYC0sf+TqcOtCDrYvIkar483hb9Mhsj8AeSWpHL6wmdN5hzHZTfjrAogNbMWIttPRaUVqfkEAEbDUd3cEQbgoPy+HyWsmkUoGwc4AxuXcRLyxOTGd2hAzNJ6AqLqdW1NdNksRpoI0HHYLDrsZp82C02HBYbNwz+n5AOyd+CveutqpfSQIjZU7n99iDosgCLUmJDSctVN/4vDenby+/f/4InItn0WBj1mLZpUEKkiqhCxJSEj4SgbubDmBe4c8gpdXw1nyqzcEojcElvta2OG3aKtrLoIVQahlImARBKHWxffsx2c915KRl8rqPctILkzChQKyjKK6cLmcKIqLc8XJvJnxER9++AU9UyP5v6f/i19gzZK11SZFUTBr7ATqguq7K4LQ6LkVsCxZsoQlS5aQnJwMQKdOnXjhhRcYPXo0UJr74KmnnmLDhg2YTCbat2/Ps88+y+23315hmy+++CIvvfTSZc+1b9+ekydPunkqgiA0dFGhscwY/dRVt/n58Fre3Pwqm9ql8ttnY/jugZ/wNfjXUQ/dk5j+G2adky7R3eq7K4LQ6Lm1/jEmJoYFCxawf/9+9u3bx9ChQ0lISOD48eMATJkyhVOnTrFq1SqOHj3Kbbfdxl133cXBgwev2m6nTp3IyMgoe+zYsaP6ZyQIwjVtUPwYVj6+nf9r9TRZviXM+vIBFEWp726V65Md7yIrMPjGm+u7K4LQ6LkVsIwbN44xY8bQtm1b2rVrxyuvvIKfnx+//vorADt37uSxxx6jV69etGrViueee46goCD2799/1Xa1Wi1NmjQpe4SFhVX/jARBaBQSBtzLtNDb2Kk5wbPLHqnv7pRrp3E/3ZW2NA1vUd9dEYRGr9oZplwuF8uWLaOkpIS+ffsC0K9fP7766ivy8/NRFIVly5ZhtVoZPHjwVds6c+YM0dHRtGrVikmTJpGSknLV7W02G0aj8bKHIAiNzz8SXuQ2/VDWOHbw1BczGtxIi0WyE+Fd+wUlBUGoRsBy9OhR/Pz80Ov1/PWvf2XFihXExcUB8PXXX+NwOAgNDUWv1/PQQw+xYsWKsrTY5enduzeffPIJP/74I0uWLCEpKYkBAwZgMpkq3OfVV18lMDCw7BEbW/PS9IIgNEwv3bOIOw0j+NG1i3s+GMNvyVe/xVxXrHYLdo0LvShgKAh1wu08LHa7nZSUFIqKili+fDkffPABP//8M3FxcTz22GPs2bOH+fPnExYWxsqVK3nzzTfZvn07N954Y5XaLywspHnz5rzxxhtMnz693G1sNhs2m63sZ6PRSGxsrMjDIgiN2Kcb3uHfyR9h8XLS2hbBhFbjmTh4Bjovz9T4cddr3z3L58ZVvN/rHXrHDa6XPgjCta5OE8cNHz6c1q1b8/TTT9OmTRuOHTtGp06dLnu9TZs2vPfee1Vus2fPngwfPpxXX321StuLxHGCcH0wFhfw+dYlrE5bR7qhkBCrgffHfEi72Kp9IfKkW5YOwiDpWT7jpzo/tiA0Fu58fte4SpqiKNhsNsxmc2mDfyq8ptFo3LrvXFxcTGJiIlFRUTXtmiAIjUyAXzCPjH2GH/+6nXe7LMQuuZizdlad9+NM6nFSdfn0Ca+4sKMgCJ7lVsAyd+5ctm3bRnJyMkePHmXu3Lls3bqVSZMm0aFDB9q0acNDDz3Enj17SExM5PXXX2fDhg2MHz++rI1hw4axePHisp+ffPJJfv75Z5KTk9m5cycTJkxAo9EwceJEj52kIAiNz4D4UdwZMZazukzyirLq7LiFpjyeXPsYBqeW+4Y+WmfHFYTrnVuJ47Kzs5kyZQoZGRkEBgbSuXNn1q9fz4gRpWXS165dy5w5cxg3bhzFxcW0adOGTz/9lDFjxpS1kZiYSG5ubtnPaWlpTJw4kby8PMLDw7npppv49ddfCQ8P99ApCoLQWA3tNJqPd37HjqMbSLhpco3aUhSF/Se3s+nEWnItefSI6Y3DZSPLlEmOOYd8Wz4XHNlkaAtRvFTmtXuK0MBroxaSIDQGovihIAjXLJfLSe+PezBA14U3p3zi1r4lFhP7Tu3g4Pk97M/ez2lSMeucyAronVosOicAkgK+Di/8FANhciDtAtoysc/9dGgRXwtnJAjXF1H8UBCE64JGo6W/VzxbnAc4nXr0qpNvTeYiNh9YzbakLRwtOUmm3ogqg6RCpCuA/r5dGdRyKEO7jcPX25/TKUcJ9AshIjgKjUa8VQpCfRMjLIIgXNMS037jnnUTkZAYYuhJh4g4/PUBeGl0ZBSlk1SQyImSM6R65eHSqBjsWtoSQ/fwrnRt3ovu7W4iwDeovk9DEK5LdbqsuSEQAYsgXN9SMs/y8g9zOMAZHNrLVyUa7FqaqeF0C+nKsLjR9Oww8IrVjIIg1A8RsAiCcF1SFIW8oiyMJYXYHFZiwlsQ4Bdc390SBKECYg6LIAjXJVmWCQ+OIjxY5HEShMZGjIsKgiAIgtDgiYBFEARBEIQGTwQsgiAIgiA0eCJgEQRBEAShwRMBiyAIgiAIDZ4IWARBEARBaPBEwCIIgiAIQoMnAhZBEARBEBo8EbAIgiAIgtDgiYBFEARBEIQGTwQsgiAIgiA0eCJgEQRBEAShwRMBiyAIgiAIDV6jqNasqipQWqZaEARBEIRrw6XP7Uuf41fTKAIWk8kEQGxsbD33RBAEQRAEd5lMJgIDA6+6jaRWJaxp4BRF4cKFC/j7+yNJEkajkdjYWFJTUwkICKjv7gnlENeo4RPXqOET16hhE9encqqqYjKZiI6ORpavPkulUYywyLJMTEzMFc8HBASIfyQNnLhGDZ+4Rg2fuEYNm7g+V1fZyMolYtKtIAiCIAgNnghYBEEQBEFo8BplwKLX65k3bx56vb6+uyJUQFyjhk9co4ZPXKOGTVwfz2oUk24FQRAEQWjcGuUIiyAIgiAIjYsIWARBEARBaPBEwCIIgiAIQoMnAhZBEARBEBq8RhewnD59moSEBMLCwggICOCmm25iy5Ytl20jSdIVj2XLltVTj68/VblGl+Tl5RETE4MkSRQWFtZtR69jlV2jvLw8br75ZqKjo9Hr9cTGxvLoo4+Kel51pLLrc/jwYSZOnEhsbCwGg4GOHTuyaNGieuzx9acq73N///vf6d69O3q9ni5dutRPR68hjS5gGTt2LE6nk82bN7N//37i4+MZO3YsmZmZl2338ccfk5GRUfYYP358/XT4OlTVawQwffp0OnfuXA+9vL5Vdo1kWSYhIYFVq1Zx+vRpPvnkEzZu3Mhf//rXeu759aGy67N//34iIiL44osvOH78OM8++yxz585l8eLF9dzz60dV3+fuv/9+7r777nrq5TVGbURycnJUQN22bVvZc0ajUQXUDRs2lD0HqCtWrKiHHgpVvUaqqqrvvvuuOmjQIHXTpk0qoBYUFNRxb69P7lyjP1q0aJEaExNTF128rlX3+vztb39ThwwZUhddvO65e43mzZunxsfH12EPr02NaoQlNDSU9u3b89lnn1FSUoLT6WTp0qVERETQvXv3y7Z95JFHCAsLo1evXnz00UdVKm0t1FxVr9GJEyd4+eWX+eyzzyotiCV4ljt/R5dcuHCB7777jkGDBtVxb68/1bk+AEVFRYSEhNRhT69f1b1GQiXqO2LytNTUVLV79+6qJEmqRqNRo6Ki1AMHDly2zcsvv6zu2LFDPXDggLpgwQJVr9erixYtqqceX38qu0ZWq1Xt3Lmz+vnnn6uqqqpbtmwRIyx1rCp/R6qqqvfcc49qMBhUQB03bpxqsVjqobfXn6pen0t++eUXVavVquvXr6/DXl7f3LlGYoSlaq6Jr65z5swpd6LsHx8nT55EVVUeeeQRIiIi2L59O3v27GH8+PGMGzeOjIyMsvaef/55+vfvT9euXZk9ezZPP/00r732Wj2e4bXPk9do7ty5dOzYkcmTJ9fzWTUunv47AnjzzTc5cOAA33//PYmJicyaNauezu7aVxvXB+DYsWMkJCQwb948Ro4cWQ9n1njU1jUSquaaSM2fk5NDXl7eVbdp1aoV27dvZ+TIkRQUFFxWyrtt27ZMnz6dOXPmlLvvDz/8wNixY7FaraLmQzV58hp16dKFo0ePIkkSAKqqoigKGo2GZ599lpdeeqlWz6Wxqu2/ox07djBgwAAuXLhAVFSUR/t+PaiN63PixAmGDBnCAw88wCuvvFJrfb9e1Nbf0IsvvsjKlSs5dOhQbXS70dDWdweqIjw8nPDw8Eq3M5vNAFfMeZBlGUVRKtzv0KFDBAcHi2ClBjx5jb799lssFkvZa3v37uX+++9n+/bttG7d2oO9vr7U9t/RpddsNlsNenn98vT1OX78OEOHDmXq1KkiWPGQ2v4bEq7umghYqqpv374EBwczdepUXnjhBQwGA++//z5JSUnccsstAKxevZqsrCz69OmDt7c3GzZsYP78+Tz55JP13PvrQ1Wu0Z+DktzcXAA6duxIUFBQXXf5ulOVa7R27VqysrLo2bMnfn5+HD9+nKeeeor+/fvTokWL+j2BRq4q1+fYsWMMHTqUUaNGMWvWrLKltBqNpkofuELNVOUaAZw9e5bi4mIyMzOxWCxlIyxxcXHodLp66n0DVn/TZ2rH3r171ZEjR6ohISGqv7+/2qdPH3Xt2rVlr69bt07t0qWL6ufnp/r6+qrx8fHqe++9p7pcrnrs9fWlsmv0Z2LSbd2r7Bpt3rxZ7du3rxoYGKh6e3urbdu2VWfPni2uUR2p7PrMmzdPBa54NG/evP46fZ2pyvvcoEGDyr1OSUlJ9dPpBu6amMMiCIIgCML17ZpYJSQIgiAIwvVNBCyCIAiCIDR4ImARBEEQBKHBEwGLIAiCIAgNnghYBEEQBEFo8ETAIgiCIAhCgycCFkEQBEEQGjwRsAiCIAiC0OCJgEUQBEEQhAZPBCyCIAiCIDR4ImARBEEQBKHBEwGLIAiCIAgN3v8HLNTWA07HmBgAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CREATE COUNTY-LEVEL MAP OF THIS STATE - excluding lakeshore tracts from county building\n",
    "for s in skipList :\n",
    "    countyNo[s] = -999  #don't count these lakeshore tracts as part of a county\n",
    "dummyPoly = Polygon([ (0,0),(1,0),(1,1)])\n",
    "countyGeom = [dummyPoly]*nCounties\n",
    "lakeErieGeom = []\n",
    "countyPop = [0.]*nCounties\n",
    "nCountyTracts = [0]*nCounties  #how many tracts found in this county\n",
    "countyTractList = [list() for c in range(nCounties) ]\n",
    "for t in range(nTracts):  #now loop through tracts, assign each to county, build county polygons\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if t not in skipList:\n",
    "        c = int(countyNo[t])\n",
    "        countyTractList[c].append(t)\n",
    "        nCountyTracts[c] +=1   #found another tract that belongs to this county\n",
    "        countyPop[c] += tractPop[t]\n",
    "        if nCountyTracts[c] == 1:  #this is the first tract found for this county\n",
    "            countyGeom[c] = tractGeom[t]  #seed the county geometry polygon\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "countyMAP = countyGeom[0]\n",
    "for c in range(nCounties):  #now build the state map from the counties\n",
    "    plotPoly(countyGeom[c])\n",
    "    plotCenter(c,countyGeom[c],8)\n",
    "    countyMAP = countyMAP.union(countyGeom[c])\n",
    "plotPoly(countyMAP)\n",
    "plt.show()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0bacf56a-d610-40d7-83a8-fe6ff799a41c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now determine the county topology\n"
     ]
    }
   ],
   "source": [
    "print(\"Now determine the county topology\")\n",
    "countyNeighbors = [list() for c in range(nCounties)]\n",
    "for c in range(nCounties):\n",
    "    for cc in range(c+1, nCounties):\n",
    "        if isRookAdj(countyGeom[c],countyGeom[cc]):\n",
    "            countyNeighbors[c].append(cc)\n",
    "            countyNeighbors[cc].append(c)\n",
    "mainMAP = countyMAP.geoms[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cda12941-0eef-4a29-a376-dd0d4bc2927b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 districts, each with avgDistrictPop of 786629.867\n",
      "I will now build a map from the counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#quick block to flag the whole county-districts -- though we do not use any whole-county units in this version\n",
    "MAP = dummyPoly\n",
    "#perfectCounty = [0]*nCounties  #\"perfect\" counties have the perfect population for a whole district\n",
    "#wholeCounty = [0]*nCounties    #\"whole\" counties are either perfect or small enough that they should be kept whole\n",
    "#minCountyRatio = 0.15   #not designated in this code\n",
    "#lockedTract = [0]*nTracts\n",
    "nDistricts = 15 #Congrl\n",
    "statePop = np.sum(tractPop)\n",
    "avgDistrictPop = statePop/nDistricts\n",
    "aDP = avgDistrictPop\n",
    "maxDevn = 0.005\n",
    "print(nDistricts,\"districts, each with avgDistrictPop of\",r3(avgDistrictPop))\n",
    "print(\"I will now build a map from the counties\") # and display the whole county districts\")\n",
    "for c in range(nCounties):\n",
    "    if MAP == dummyPoly:\n",
    "        MAP = countyGeom[c]\n",
    "    else:\n",
    "        MAP = MAP.union(countyGeom[c])\n",
    "    #if countyPop[c] >= (1.-maxDevn)*avgDistrictPop and countyPop[c] <= (1.+maxDevn)*avgDistrictPop :\n",
    "    #    perfectCounty[c] = 1\n",
    "    #    wholeCounty[c] = 1\n",
    "    #    plotPoly(countyGeom[c])\n",
    "    #if countyPop[c] < minCountyRatio*avgDistrictPop:  #small counties will naturally be unsplit.\n",
    "    #    wholeCounty[c] = 1\n",
    "plotPoly(MAP)\n",
    "plt.show()\n",
    "#print(\"there are a total of\",np.sum(wholeCounty),\"small or perfect counties that we will keep whole in Home Districts\")\n",
    "#for t in range(nTracts):\n",
    "#    if isSkippedTract == 0:\n",
    "#        if wholeCounty[countyNo[t]] == 1:  #this tract is part of a whole county per Article XI.3.c.2\n",
    "#            lockedTract[t] = 1             # ... or in a county that is so small we are keeping that county whole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "732d8c17-022d-4fe9-b36d-2a15748d8d41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,v in enumerate(skipList):\n",
    "    plotPoly(tractGeom[v])\n",
    "    plotCenter(v+0.1*i,tractCP[v])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "29bf3b47-56e7-4d50-939a-af1c51b28b81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we need to read in the school district shapes and assign VTDs to SDs\n",
      "assigning VTDs to school district number 0\n",
      "assigning VTDs to school district number 100\n",
      "assigning VTDs to school district number 200\n",
      "assigning VTDs to school district number 300\n",
      "assigning VTDs to school district number 400\n",
      "skipping Ohio school district 499 it is a Lake Erie faker\n",
      "assigning VTDs to school district number 500\n",
      "assigning VTDs to school district number 600\n"
     ]
    }
   ],
   "source": [
    "print(\"Now we need to read in the school district shapes and assign VTDs to SDs\")\n",
    "sdDF = gpd.read_file(\"./state_map_files/tl_2021_39_unsd.dbf\")\n",
    "sdGeom = sdDF[\"geometry\"]\n",
    "nSDs = len(sdGeom)\n",
    "nCOIs, COIgeom = nSDs, sdGeom.copy() #nomenclature\n",
    "sdVTDlist = [list() for s in range(nSDs)]\n",
    "tractMuniNo = [-999]*nTracts\n",
    "for s in range(nSDs):\n",
    "    if s%100 == 0:\n",
    "        print(\"assigning VTDs to school district number\",s)\n",
    "    if sdGeom[s].contains(tractCP[skipList[4]]):\n",
    "        print(\"skipping Ohio school district\",s,\"it is a Lake Erie faker\")\n",
    "        faker = s\n",
    "    else:\n",
    "        for c in range(nCounties):\n",
    "            if sdGeom[s].intersects(countyGeom[c]):\n",
    "                for v in countyTractList[c]:\n",
    "                    if sdGeom[s].contains(tractCP[v]):\n",
    "                        sdVTDlist[s].append(v)\n",
    "                        tractMuniNo[v] = s\n",
    "muniTractList = [sdVTDlist[s].copy() for s in range(nSDs)] #nomenclature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "970e7243-18d5-4f72-8517-29263b393a0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPoly(MAP,0.1)\n",
    "plotPoly(sdGeom[499])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "97b6f366-d522-4b4b-b788-c0364dbdfaf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 612 COIs / school districts in the shapefile, including the Lake Erie faker\n",
      "There were a total of 20 low-pop non-coastal vtds not captured by a schoolD\n",
      "[937, 1006, 1415, 3622, 3627, 3778, 3852, 3854, 3948, 3956, 3959, 4006, 4014, 4433, 4438, 4559, 4653, 5134, 5159, 5456]\n",
      "I will now assign them to the contiguous SD with the closest centroid, excluding the faker\n"
     ]
    }
   ],
   "source": [
    "print(\"there are a total of\",nCOIs,\"COIs / school districts in the shapefile, including the Lake Erie faker\")\n",
    "missedVTDlist = list()\n",
    "for v in range(nTracts):\n",
    "    if v not in skipList and tractMuniNo[v] < 0:\n",
    "        missedVTDlist.append(v)\n",
    "print(\"There were a total of\",len(missedVTDlist),\"low-pop non-coastal vtds not captured by a schoolD\")\n",
    "print(missedVTDlist)\n",
    "print(\"I will now assign them to the contiguous SD with the closest centroid, excluding the faker\")\n",
    "for b in missedVTDlist:\n",
    "    closeCOIs = list()\n",
    "    for c in range(nCOIs):\n",
    "        if isRookAdj(tractGeom[b],COIgeom[c]) and c != faker:\n",
    "            closeCOIs.append(c)\n",
    "    closeDists = [tractGeom[b].distance(COIgeom[c].centroid) for c in closeCOIs]\n",
    "    cc = closeCOIs[closeDists.index(np.min(closeDists))]\n",
    "    tractMuniNo[b] = cc\n",
    "    muniTractList[cc].append(b)\n",
    "    COIgeom[cc] = COIgeom[cc].union(tractGeom[b])\n",
    "\n",
    "for v in range(nTracts):\n",
    "    if tractMuniNo[v] < 0 and v not in skipList:\n",
    "        plotPoly(tractGeom[v])\n",
    "        print(\"unassigned noncoastal vtd, its county and pop\",v,countyNo[v],tractPop[v])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d244db11-e615-4b99-bd0c-c716575410ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a histogram of number of vtds per COI\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and the number of vtds vs. COI index\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the minimum number of vtds in a COI is 0\n",
      "COI 433 was assigned no vtds\n",
      "COI 434 was assigned no vtds\n",
      "COI 499 was assigned no vtds\n"
     ]
    }
   ],
   "source": [
    "print(\"here is a histogram of number of vtds per COI\")\n",
    "plt.hist([len(muniTractList[u]) for u in range(nCOIs)],bins = [0,1,3,10,30,300,1000] )\n",
    "plt.show()\n",
    "print(\"and the number of vtds vs. COI index\")\n",
    "plt.scatter([u for u in range(nCOIs)], [len(muniTractList[u]) for u in range(nCOIs)] )\n",
    "plt.show()\n",
    "print(\"the minimum number of vtds in a COI is\",np.min([len(muniTractList[u]) for u in range(nCOIs)]) )\n",
    "emptyCOIs = list()\n",
    "for u in range(nCOIs):\n",
    "    if len(muniTractList[u]) == 0:\n",
    "        print(\"COI\",u,\"was assigned no vtds\")\n",
    "        emptyCOIs.append(u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f0874ac8-8ddc-4a2d-863a-96dd96b7529f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for u in [408,433,434]: #,499]:\n",
    "    plotPoly(sdGeom[u],0.5)\n",
    "    for v in muniTractList[u]:\n",
    "        plotPoly(tractGeom[v],1)\n",
    "        plotCenter(u,tractGeom[v])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d6d5af15-5f36-4849-8637-0dcb35774f76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ok, we will jettison the empty COIs / school districts and renumber\n",
      "ditching empty COI 433\n",
      "ditching empty COI 434\n",
      "ditching empty COI 499\n",
      "we found 8934 vtds in a COI, and there were 7 in the skipList. Total VTDs = 8941\n"
     ]
    }
   ],
   "source": [
    "print(\"ok, we will jettison the empty COIs / school districts and renumber\")\n",
    "oldTractList = [muniTractList[s].copy() for s in range(nCOIs)]\n",
    "muniTractList = list()\n",
    "for s in range(nCOIs):\n",
    "    if len(oldTractList[s])  > 0 :\n",
    "        muniTractList.append(oldTractList[s].copy())\n",
    "    else:\n",
    "        print(\"ditching empty COI\",s)\n",
    "nCOIs = len(muniTractList)\n",
    "tractMuniNo = [-999]*nTracts\n",
    "for s in range(nCOIs):\n",
    "    for v in muniTractList[s]:\n",
    "        tractMuniNo[v] = s\n",
    "nFound = 0\n",
    "for v in range(nVTDs):\n",
    "    if tractMuniNo[v] >= 0 :\n",
    "        nFound += 1\n",
    "print(\"we assigned\",nFound,\"vtds to a COI, and there were\",len(skipList),\"in the skipList. Total VTDs =\",nVTDs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d61239cc-cf27-49bc-88ff-60617d317e31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we now have 609 (nonempty) COIs\n"
     ]
    }
   ],
   "source": [
    "print(\"we now have\",nCOIs,\"(nonempty) COIs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f9a51f78-9081-47e9-9ca0-21314c7e451f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on vtd neighbors in county 0 time (sec) is now 0\n",
      "working on vtd neighbors in county 10 time (sec) is now 16\n",
      "working on vtd neighbors in county 20 time (sec) is now 90\n",
      "working on vtd neighbors in county 30 time (sec) is now 160\n",
      "working on vtd neighbors in county 40 time (sec) is now 187\n",
      "working on vtd neighbors in county 50 time (sec) is now 216\n",
      "working on vtd neighbors in county 60 time (sec) is now 237\n",
      "working on vtd neighbors in county 70 time (sec) is now 247\n",
      "working on vtd neighbors in county 80 time (sec) is now 283\n",
      "finding Kelleys Island\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#############3 alt to below -- read in a previously established vtd topology, including island connections\n",
    "startTime = time.time()\n",
    "vtdNbrs = [list() for v in range(nTracts)]\n",
    "for c in range(nCounties):\n",
    "    if c%10 == 0:\n",
    "        print(\"working on vtd neighbors in county\",c,\"time (sec) is now\",int(time.time()-startTime) )\n",
    "    for v in countyTractList[c]:\n",
    "        for vv in set(countyTractList[c]).difference({v}):\n",
    "            if isRookAdj(tractGeom[vv], tractGeom[v]):\n",
    "                vtdNbrs[v].append(vv)\n",
    "        for cc in countyNeighbors[c]:\n",
    "            if isRookAdj(countyGeom[cc],tractGeom[v]):\n",
    "                for vv in countyTractList[cc]:\n",
    "                    if isRookAdj(tractGeom[vv], tractGeom[v]):\n",
    "                        vtdNbrs[v].append(vv)\n",
    "for v in countyTractList[21]:\n",
    "    plotPoly(tractGeom[v])\n",
    "    plotCenter(v,tractGeom[v])\n",
    "print(\"finding Kelleys Island\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a94e8cb9-605f-481b-a003-7ab6d02a319a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HhnTauUVE2tMj/9nEywu2U+P1cfbQFJ65fASDUjX6SUSOTqGmneyr9LBwWzGLtxWzJLeELYVVAGTEhjK6VxxXj+3JsIxoesWHW3aVZ5HW+HxtAXERwXx27eg2XwlcRKxNoaaNGIbBvM37+HZzEd9vLWJjQSVgduId3SuWm07vq/4vIsfgylN68qfZa/H5u8XATBFpQwo1beTxL3P469zNpEaFMHb/AoGn9IkjMTIk0EUT6VLOPzGNez5ax4cr8/n1pH6BLo6IdCEKNW1g8bZi/jp3M7+Z1I9bJ/dTXxiR4zB/8z68PkMLXIpIix1XZ44HH3wQm83GLbfc0rjv2WefZcKECURGRmKz2SgrKzumc+3evZvLLruMuLg4QkNDGTJkCD/88EPj+4Zh8Oc//5mUlBRCQ0OZPHkyW7ZsOZ7it5mf719g8jcT+yrQiBynmPBgADYWVAS4JCLS1bQ61CxdupRnnnmGoUOHNtlfU1PD1KlTufPOO4/5XKWlpYwdO5agoCA+++wz1q9fzyOPPEJMTEzjMQ8//DCPP/44Tz/9NIsXLyY8PJwpU6ZQV1fX2ktoMz3jzM6M5bXeAJdEpOsb0zuOS0b14P5PNrCjuDrQxRGRLqRVyyRUVVUxfPhwnnrqKe677z6GDRvGY4891uSYefPmcfrpp1NaWkp0dPQRz/eHP/yBBQsW8O233zb7vmEYpKam8rvf/Y7bb78dgPLycpKSknj55Ze5+OKLj1rm9lwmYcXOUi59bjE+v8GUwcmce0Iqo3rHamZfkVaq9jQw9qGvmD4khfvPHxLo4ohIALXk97tVNTU33XQT06dPZ/Lkya0q4H/78MMPGTlyJD/96U9JTEzkxBNP5Lnnnmt8Pzc3l4KCgibfFxUVxejRo1m4cGGblOF4nNgjhu//MJE7pmaxLr+ca1/9gWH3/Iezn/iW295aycOfb+SfC7czZ/1e1uWX49eoDpEjCnc5CQ1yoP+niEhLtLij8Jtvvsny5ctZunRpmxVi27Zt/OMf/+C2227jzjvvZOnSpfzmN78hODiYK6+8koKCAgCSkpKafC4pKanxvf/m8XjweDyNrysq2rd9PiY8mGtP7c0143qxs6SGRduKWZxbws7iGhbnlrC3oq5xqQO7DV6/bgyje8WqD47Ij6io9dI7PjzQxRCRLqRFoSYvL4/f/va3zJkzh5CQthuq7Pf7GTlyJP/7v/8LwIknnsjatWt5+umnufLKK1t1zgceeIB77rmnzcp4rGw2G5lx4WTGhfPzkw5O7e73GxRVe5i7vpA7/72Gi59dRGSIk76JEfRLdNMvKYI+iRH0S4wgLTqUXaW17Cmvo7jKw8iesVpxW7odV5CDz9cWMKZ3HIPTNKOwiBxdi0LNsmXLKCwsZPjw4Y37fD4f8+fP58knn8Tj8eBwOFpciJSUFAYOHNhk34ABA3jvvfcASE5OBmDv3r2kpKQ0HrN3716GDRvW7DlnzZrFbbfd1vi6oqKCjIyMFpetrdjtNhLdIVw6ugfj+sazoaCCnMIqcgqrWLennNmrdlPn9f/o5wemRHL9+N6cPTRFMxBLt3D3OQN56LONnP3Ed4zIjOHC4ekMTY+iX1IELmfL/zsjItbXolAzadIk1qxZ02Tf1VdfTXZ2NjNnzmxVoAEYO3YsmzZtarJv8+bNZGZmAtCrVy+Sk5P58ssvG0NMRUUFixcv5sYbb2z2nC6XC5erc9Zu9IgLo0dcGFMGHdx3YIXuLYWVbNhTSaLbRUZsGJlxYSzJLeGDFbu55a2VPDpnMzdO6MMFw9P0H3axtHOHpTF9SApzN+zl5e+3c9cHa/AbEOSw0S/RzaDUSIakR3HmwGSSozTJpYi0cvTToSZMmNBk9FNBQQEFBQX88MMPXHfddcyfPx+3202PHj2IjY0FzHB0/vnnc/PNNwPm8PBTTjmFe+65h5/97GcsWbKE6667jmeffZYZM2YA8NBDD/Hggw/yyiuv0KtXL/70pz+xevVq1q9ff0xNYe05+qmjrN1dzlPzcvhsbQFJ7hCuPbUX552YRnxE5wxvIm2ppr6BDXsqWZ9fztrdFazbU86mgkp8foMzBibxt4tPJCRIQV/Ealry+93mMwo//fTTTfqyjB8/HoCXXnqJq666CoCtW7dSVFTUeMxJJ53Ev//9b2bNmsW9995Lr169eOyxxxoDDcAdd9xBdXU1119/PWVlZYwbN47PP/+8Tfv2dHaD06J4asYIcgoreWreVh74bCP/++kGRvaMZeqgZMb3j6dXfAQOuzofi/WEBTsZkRnDiMyD81eV13q58V/L+GLdXnaV1tI3MSKAJRSRQDvumpquwgo1Nf+tuMrD3A17+XxtAQtyiqn3+QkJsjM0PZoLTkzjwhHpBO3vf2MYBkVV9dT7zH47QQ4bceGuxgBkGIZGYkmnVOf1sa/SQ2Glh32VHvZV1rGnvI41u8tZlVdGRV0Do3rF8sZ1YxToRSyoJb/fCjUWUVnnZc3uctbnV3DfJxsAGNUzltOyEli/p4LF20ooqvI0+UxokINBqZGU1NSzq7QWn9/A5bTv3xy4gsznocFOeseHMyg1ksjQIKrqGqj1+qit9xESZKdnfDg948IJC3bgtNtxBdkJOeTzCktyLDYWVPDxqj3sKq1pDDCFlZ7DZup22m0kul0MSIlkWEY0J2REM7p3rPqYiViUQk0zrB5qDrViZynnP/U9AO4QJwNSIjkxI5rhmTGE7u9z4Gnws21fFevyK4iPcJEeE0qw046nwY+nwYfH6298XlXXQM6+KtbnV+BpMGuDwoLNydGq6xsoqzny8hAup52QIAchQeZjaJCDxMgQ0qJDSIkKJdHtIjY8mLgIFylRISS6XRrh1c2c++R3rNpVTpDDxrCMaBLdISS4XSRGug4+37/FhAVjV42MSLcR0D41Engn9ohh2/+eRVGVhwS36wg1JUk/sr959Q1+bDYam7QOKKupZ3txDR6vD5/foG5/KKpr8FHn9VPnNR89+1/X1DdQUF7H2t0V/GfdXoqr65ucz26DpMgQUqJCSIkO5dS+8ZyQEU1GbBgRLv3JWonX5+fZ+dtYtascgB/+eAZRYVpeRERaR78QFmW320iMbNtO1MHO5mtPosOCGRYW3OrzNvj8lNV62VfpoaCijj1ldewpryW/rI6dJdX84f2D0wgkRbp4+4aTqahtYPbK3TT4DSJDnESFBRMdGkRUaBD1Pj9en58Il5Nwl5MIl5PY8GBSokLUFNaJfL62gD/NXsu+Sg8/OSGVmdOyFWhE5Lgo1EjAOR124iNcxEeY/ST+W3GVh/s/3cD7y3dTWOnhpQXbefuHPNwhTqJDg6ms81JW66Wm3nfE74kMcTK6dxxXn9ITr9+gwefH6zOIDHUyKDWKqFD9oHYEwzD4xzdbefjzTZw5MInfnZlFVrI70MUSEQtQqJFOLy7CxaiesewqraWkup6PV+9h6qBk7j9/CKHBBzuHehp8lNd6cTkcOBw2ajwNVHkaqPb42FtRx8aCCt76IY9Ln1/c7Pf0iA1jbN84fn5SD4ZlRHfQ1XUPfr/BuvwK3lu+i2U7Slmzu5zfTOzLLZP7q3+MiLQZdRSWbqXO62PbvmqiwoIIctgIstspqvKwLr+C1bvK+WJdAbvLapmYnchfzhlEj7gwAEqq63nl++0YhkFdg5+KWi9lNV56xofz+ylZGkr8X6o8DazcWcayHaUs21nKip2lVNY1kBTpYkzvOM4emsoZA1vWp0tEuieNfmqGQo0cC7/f4OM1e3jg0w2UVNfz4lUnUVxdzyer8/li3V4AesWHExnipKiqnt1ltfSMC+N/LxjCKX3iA1z6wKvz+vjjv9fy7xW78BsQFRrE8B7RDO9hTpp3Uq/Ywzqai4gciUY/ibSS3W5j3qZC9pTXATCjmaaqP541gMn7axlW7yrjfz/dwKXPLebpy0YwdXByh5a3s/m/Lzbx0ap87po+kPH94+kdH6HmJRHpMPonk8h/GdMr7rB9adGhPHnpiUwdlMyNry3jyw1mrc3Q9Gjumm6uMF9QXtuh5exsvtq4l+e/y+WOqVn8Ylwv+ia6FWhEpEOppkbkv/zspAx+dlIGYI7Uuf+TDTz/XS7xES6euPREbn59OTf+azm/O7M/DruNR+dsBmBidvfsI1LlaeCJL7fw7LfbOD0rgV+M7RXoIolIN6VQI3IENpuNP04fwIKtxTw1byuv/mIUT1wynFveWsEDn20kwuUkJMjRbVdKX7u7nLOf+A6AP0zL5rpTe6t2RkQCRqFG5ChsNhs3jO/NLW+tZPWuMoamR/PUjBHUN/h/dELC7sAwDFbvnwkY4Kcj0jUKTEQCSqFG5BhMGpAIwE+eXMAXt4wnK9ndZQKNYRiU1njJL6slNjyY5MiQFtemeBp87CmrY3txNQtyilixs4xNBZVUehoAuOqUnsSGt35WaRGRtqBQI3IMbDYbY/vGsSCnmCmPzWdkZgw/HZnOST1j6RUf3mmWX2jw+VmcW8Lnawv4amMh+yo91Pv8TY4JdtoZkhZFanQowQ47/ZIiGJYRTVmNl2pPAzX1DRRWethVWsuu0hrySmrZW1nHgckfkiJdjO4Vx8QBiQxNi2ZkzxhCgrRCtogEnuapEWkBr8/P3PV7+dfiHSzIKQbMuVh6J4TTMy6czLiwJo/RYUHtGnjqvD627qtiQU4Ra3dXsCCniOLqetJjQpk8IIk+CeEEOeyEu5z0iA2jpLqe3KJqluSWUF7rpa7Bx7rdFU2Cj8NuI9FtrtyeHhNGxv7H9EMe1W9GRDqKJt9rhkKNtLXyGi+rdpWxelcZ24tr2FFczfbiGvZVehqPcYc4yYgJIynSRaI7hKRIFwmRIThsNmrqG6it91Hj9ZmP9Q3U1Puo8/oICXIQFx5MbLiL2PAg7HYbfr9BUVU9BeV1rNtTTk5hFXVeM4yEBjkYlBrJiJ4xTB+SwpC0qGMOU8VVHlbvLmdQaiRRoUEEO+ydpuZJREShphkKNdJRqjwN7Dwk5OwqraGw0kNhRZ35WOnB5zcIC3YQFuwgNNhBWJDTfAx2EBrkoKbeR0l1PcXV9ZTW1GMYBnabzewTExVC/yQ32clu4iKCiQs3lx7oKn18RERaQjMKiwRQhMvJwNRIBqY2/38+v9/8d4SacERE2pZCjUgHU5gREWkfqq8WERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUs4rlDz4IMPYrPZuOWWWxr3Pfvss0yYMIHIyEhsNhtlZWVHPc9f/vIXbDZbky07O7vJMRMmTDjsmF/+8pfHU3wRERGxEGdrP7h06VKeeeYZhg4d2mR/TU0NU6dOZerUqcyaNeuYzzdo0CDmzp17sGDOw4t23XXXce+99za+DgsLa0XJRURExIpaFWqqqqqYMWMGzz33HPfdd1+T9w7U2sybN69lBXE6SU5OPuIxYWFhRz1GREREuqdWNT/ddNNNTJ8+ncmTJ7dZQbZs2UJqaiq9e/dmxowZ7Ny587BjXnvtNeLj4xk8eDCzZs2ipqbmR8/n8XioqKhosomIiIh1tbim5s0332T58uUsXbq0zQoxevRoXn75ZbKystizZw/33HMPp556KmvXrsXtdgNw6aWXkpmZSWpqKqtXr2bmzJls2rSJ999/v9lzPvDAA9xzzz1tVkYRERHp3GyGYRjHenBeXh4jR45kzpw5jX1pJkyYwLBhw3jssceaHDtv3jxOP/10SktLiY6OblGhysrKyMzM5NFHH+Waa65p9pivvvqKSZMmkZOTQ58+fQ573+Px4PF4Gl9XVFSQkZFBeXk5kZGRLSqPiIiIBEZFRQVRUVHH9PvdopqaZcuWUVhYyPDhwxv3+Xw+5s+fz5NPPonH48HhcLSu1IeIjo6mf//+5OTk/Ogxo0ePBvjRUONyuXC5XMddFhEREekaWhRqJk2axJo1a5rsu/rqq8nOzmbmzJltEmjA7Ii8detWLr/88h89ZuXKlQCkpKS0yXeKiIhI19aiUON2uxk8eHCTfeHh4cTFxTXuLygooKCgoLGWZc2aNbjdbnr06EFsbCxghqPzzz+fm2++GYDbb7+dc845h8zMTPLz87n77rtxOBxccsklAGzdupXXX3+ds846i7i4OFavXs2tt97K+PHjDxtSLiIiIt1Tq+ep+TFPP/10kw6648ePB+Cll17iqquuAsyQUlRU1HjMrl27uOSSSyguLiYhIYFx48axaNEiEhISAAgODmbu3Lk89thjVFdXk5GRwYUXXshdd93V1sUXERGRLqpFHYW7spZ0NBIREZHOoSW/31r7SURERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELEGhRkRERCxBoUZEREQsQaFGRERELOG4Qs2DDz6IzWbjlltuadz37LPPMmHCBCIjI7HZbJSVlR31PH/5y1+w2WxNtuzs7CbH1NXVcdNNNxEXF0dERAQXXnghe/fuPZ7ii4iIiIW0OtQsXbqUZ555hqFDhzbZX1NTw9SpU7nzzjtbdL5BgwaxZ8+exu27775r8v6tt97KRx99xDvvvMM333xDfn4+F1xwQWuLLyIiIhbjbM2HqqqqmDFjBs899xz33Xdfk/cO1NrMmzevZQVxOklOTm72vfLycl544QVef/11Jk6cCMBLL73EgAEDWLRoEWPGjGnxNYiIiIi1tKqm5qabbmL69OlMnjy5zQqyZcsWUlNT6d27NzNmzGDnzp2N7y1btgyv19vk+7Kzs+nRowcLFy5sszKIiIhI19Ximpo333yT5cuXs3Tp0jYrxOjRo3n55ZfJyspiz5493HPPPZx66qmsXbsWt9tNQUEBwcHBREdHN/lcUlISBQUFzZ7T4/Hg8XgaX1dUVLRZeUVERKTzaVGoycvL47e//S1z5swhJCSkzQoxbdq0xudDhw5l9OjRZGZm8vbbb3PNNde06pwPPPAA99xzT1sVUURERDq5FjU/LVu2jMLCQoYPH47T6cTpdPLNN9/w+OOP43Q68fl8bVKo6Oho+vfvT05ODgDJycnU19cfNpJq7969P9oPZ9asWZSXlzdueXl5bVI2ERER6ZxaFGomTZrEmjVrWLlyZeM2cuRIZsyYwcqVK3E4HG1SqKqqKrZu3UpKSgoAI0aMICgoiC+//LLxmE2bNrFz505OPvnkZs/hcrmIjIxssomIiIh1taj5ye12M3jw4Cb7wsPDiYuLa9xfUFBAQUFBYy3LmjVrcLvd9OjRg9jYWMAMR+effz4333wzALfffjvnnHMOmZmZ5Ofnc/fdd+NwOLjkkksAiIqK4pprruG2224jNjaWyMhIfv3rX3PyySdr5JOIiIgArRzSfSRPP/10k74s48ePB8wh2FdddRUAW7dupaioqPGYXbt2cckll1BcXExCQgLjxo1j0aJFJCQkNB7z17/+FbvdzoUXXojH42HKlCk89dRTbV18ERER6aJshmEYgS5ER6ioqCAqKory8nI1RYmIiHQRLfn91tpPIiIiYgkKNSIiImIJCjUiIiJiCQo1InJk3jooWAPVRUc/VkQkgNp89JOIdGF1FbBzIeQthsKNsG8jlOaC4YfQWLjsPUgbHuhSiog0S6FGpDurrzZDTO63sP1byF8Jhg8ikiFpEPSfCglZENsbvrwHXjkHLn0beo4NdMlFRA6jUCPS3fj9sOU/sOgp2PE9+L0Qngi9ToXhV0DPU80QY7M1/dwVs+GNS+CNi83nqrERkU5GoUaku2ioh7XvwoLHYd8GSD8Jpj4AvcZDfP/DQ8x/Cw6Hi1+DV8+Dl6bBOX+DEy7ukKKLiBwLhRoRq/NUwrJXzJqZit3Qbwqc/Sj0OPnoQea/udxw1cfwye/g3zfA7uUw5X5wBLVP2UVEWkChRsSqakrg+8fhhxfNvjNDfgqn/AaSBh7feYNC4dy/m81Pn82EmmKY/giERrdJsUVEWkuhRqSr8/uhvsocdm34wOaA6n3wzpXm+30nm01FUelt9502G5x0LYREw+ybIWcujP0tjL7BbKYSEQkAhRqRrqa2DL55yAwy6z4AT0Xzx9mDILoHnP1Y2waaQw25CHqOg/n/B1//L/zwElzwLGSe3D7fJyJyBFrQUqQrqdgDj2YffD3+9xCVAUFhkJgNzlDAgNAYCI/v2LKV5MK7V0P+CrPfzqQ/Q/Lgji2DiFhOS36/VVMj0pWEREFYPNQUQf9pMPYWcEUEulSm2F5w7Vew7n34+n54epxZk3P6neYQcRGRdqZlEkS6kuAw+H0O/OyfsPkz2PBRoEvUlN1uBpmblsDZf4Xt38GTJ8HHt0FlQaBLJyIWp1Aj0tXYbDDwJ+CKgurCQJemeY4gGHk1/GaF2Qy19j342zCYc7c5KktEpB0o1Ih0VWGxnX+RyaBQc1TUb1fByTfBkmfhr4Ph8zuhfHegSyciFqNQI9JVJQ+B9R+AtzbQJTm60GiY9Ce4ZQ2c/CtY+S/42wkw+yYo2hLo0omIRSjUiHRVk/9i9lOZ//8CXZJjFx4PE++CW9aazVJb5pp9bt663Bw1JSJyHBRqRLqquD5m0863j5jzxPh9gS7RsQuJhLG/MZulzv6rOXHgsxPMdaW2fQPdY6YJEWljmqdGpCvz+2HOn2Dh382QM/4OGHCOOUqqK/H7zKa0b/8Ke9dA2ggYdytkTTdHVIlIt9WS32+FGhEr2PUDfPMwbPkCgsIhezoMPNdcidudFOjSHTvDgJwv4btHYccCiM+CcbeY61Zp0UyRbkmhphkKNdItlOTC2ndh9TtQtMncF5kOqcPMCfBie0FMT4jOhPAEc9Xtlq7U3VF2LjbDzebPzWs45dcw/IquVwslIsdFoaYZCjXSrRgGlOfB7uWwexkUrDYDT/kuc9HLA+xOCI01Ryc5XeAI3r8FgcN18HlwuDmbcUQiZJ8D8X077lr2roPvHjPnugmNhtE3wqhrzaUgRMTyFGqaoVAjAvi8Ztgp2wk1xeZEeLWl5iKZvnrwecxjfPX7Ny80eKC+GurKoCLfXEgzZRj0Pg16ngo9xpg1Pu2tdDt8/wQs/+fByf1Ovhncye3/3SISMAo1zVCoEWkD3lrY9Cls/NRcAqFq/9IHNru5Krg7CU683Gwmaq+wUVUIi56CpS9AQx0Mu9QcBab1pUQsSaGmGQo1Im3MMKA4x+yk7K0BfwPsXQtr3jVreQacAyddB5mntE+/nbpyM9gsesqsbRr9S3PV8tDotv8uEQkYhZpmKNSIdJDaMlj1Jix9Hoq3QMIAOOkaGPpzc36atuathYVPwrePgs0Bwy+H0TeYHaJFpMtTqGmGQo1IBzMMyJ0PS58zm6uCQs1gc9K1kDSw7b+vssBcW+qHF81anKyzzPWmepzceUd4ichRKdQ0Q6FGJIDKd8PyV2DZy1C1FzLHmrU3A89v+8n16mtg9Zuw6B9QtNns1Dz6Bhh4noaDi3RBCjXNUKgRCTDDMEcwzf2LOXswwM/+CQN/0j7f5/fD1q9g0d/NR1ekOVrrvKfU70akC1GoaYZCjUgA+f3w10FQmX/4e1nTYfojEJnSft9fkgsLHjNrigD6T4PTZ0HKCe33nSLSJlry+61FVUSk/dlsMOg887nDBX0nm3PMnHEv7FoKfx+9f4h2fft8f2wvOOdvcPXncOrvoHAdPDMeXjrL/N7asvb5XhHpUKqpEZGOYxiHd9qtKYEv/girXofItEOWQwhvv3J4qmD9bFj3vtk05Qg218o68TKziUodi0U6DTU/NUOhRqSTK9xoNhGtftvs8zLmVzDmxvYNNwAVe2DVG7DiX1CyFdJHwZT7IWNU+36viBwThZpmKNSIdBGlO2DB32DFP811qSb8wZyl2OFs3+81DNj2Ncz5MxSsgX5TYNT10Gdi24/QEpFjplDTDIUakS6mdAd8dR+seRvi+8O0h8yA0d78fvM7Fz5phpuYXubw82EzICy2/b9fRJpQqGmGQo1IF7VnldnnZvt3cNpMs6OvM7j9v9cwIG+JOXngug/A7oAhF5lLP6QOa//vFxFAoaZZCjUiXZjfD9/+H8x70Ky1ufJDiEjsuO+vKoTlr8IPL0HFLnP01oRZkDZCnYpF2plCTTMUakQsYNcyeHk6xPeDC1+AhP4d+/2+BtjwIXz1P1CyzQxYQ39ubtEZHVsWkW5CoaYZCjUiFpEzF/51ofl8/B3m6tzhcR1bBl8D5M6DVW/Bho+godYcCj5shtlE5Qjq2PKIWJhCTTMUakQspHgrPDHcfO4MgZnbzQUzA8FTaQabVW+YC3hG9YCRV5kjtjqyiUzEohRqmqFQI2Ix3jq4P8l8/qfi9h/yfSwK1pqjptb9G/wNkH02jLwaeo7XsHCRVtIyCSJifUEhMO1h8/kTw+G7x8xmoUBKHgznPw23bYAz74PCDfDqufDkSHNyP583sOUTsTjV1IhI17Z7OXx2h7mGVEQynPZ7OPGKjhn2fTSGATsXmSuFb/gI3KmQMhRGXWeOoBKRo1LzUzMUakQszDDMcLPkWVj9FsRkwoQ7zU67dkegS2cqWAMrX4cdC8y5d3qcAiOuMicUjEgIdOlEOi2FmmYo1Ih0E4UbzJmIN34MSYPhp69AfN9Al+ogv88s28K/Q95isNmh35kw/ErzsTP0DRLpRBRqmqFQI9LN7PoBnp8E4Qlw+5bOOUle5V4z4Cx/FfasNJvPTpxhBpyYzECXTqRTUKhphkKNSDe09Sv45/kw9SEYfUPnDDYH7FllhpvV74CnAvqdYdbcDPmpuWq5SDelUNMMhRqRburD38DyV8zaj588HujSHF19Nax+G9a8C3mLwB4EQy6EQRdA79M1NFy6HYWaZijUiHRThgHvXWvORPybFV1rpe3KvbDin7DsZSjPg8xx0P9MGHwhRKUHunQiHUKhphkKNSLdWMUeeGwwhCdC9lngTjb7r0SlQeJA83Vn5vfD+g9g6fOQvxJ8Hhh8EQy/HDLGqHOxWJpCTTMUakS6uTXvwpp3oHw3VO6BmqKD7w08Dy54rnPMbXM0nkqz782if5i1NyFR5szFQy4yZy5WwBGLUahphkKNiDTRUG+Ggtz58PEt5qrfQy4KdKmOnd8Pe1bAps9g7ftQshUi02HcLeZ1hMYEuoQibULLJIiIHI0zGOL6mAtiAoR18Erfx8tuh7QRMPEu+PUyuO4ryBgFn82E/8uCd66Czf8xOx6LdBOqqRGR7u0vUebj3WWde8j3saoqNGdVXvEa7NtgTu434Ccw/nZzMkIrXKN0Ky35/Vbjq4h0X36f+egMBcMPtk6ypMLxiEiEU34NJ99szq68/Tv49hF4ehxE9YC+E82lGXpPMPvjiFiIQo2IdF8HQk1DrTmjb9qIgBanTdlskDTQ3EZeDdvmwZY55oSEy14Ghwuyp8PUBzr/6C+RY6RQIyLdlzMYRv4C1n8IyScEujTtxxG0f4biM8zXpTvg81mw7n1z6z8Vhs2AzLEQ3sX6FokcQqFGRLqv+hpzqPeo67vXUOiYTLjkdagpMdeeWvwMvH252Qw38hfQc5wW15QuSX+xItJ9rZ9trrM0/PJAlyQwwmJh+BXmVrINvn8CNnwEi/4O0Zlm3xwND5cuRKOfRKT7enEqOF1wxexAl6RzyV8J3z8O6/5tLjORNAj6TjIX19QIKulgmqdGRORoSnfAzoUw7LJAl6TzSR0GF70It6yF856ClBNg+T/NEVRPnQwL/w61ZYEupchh1PwkIt3T+tnmxHtZUwNdks4rKg2GXWpuPi/kfGnOgfPFH+E/f4LMU2D0DZA1XauHS6egUCMi3dP6D6DvZHC5j3poWWku23cvNF/sb7E3Gh/95iNN95v7/E0/Q9PPAGT1OoPomF6tv46O4ggyA2DWVDjzf2DzF2Yn67cug8g06HUa9BgDA8+F0OhAl1a6KfWpEZFuY1v5Nu75/h4q6krwFW0h0u8nNTSBu89/l3B3SpNj8/IW8NXqV/h633JWUIe/nfqRBBkGExwxnNf/Ak4ZfiPOoJB2+Z52s+sHM9zkLYI9q8AeBFnTzNqd3qd3jUVCpVPTgpbNUKgRsZYNxRvYVr6NfTX7KKwtNB9rCtlXuw/DMIgNjSU2JJaE0ATGpo1lXNo4rv/P9eyt2cvpaadi27OKH8pz2GDz8uboexiUfQG7di3i/SWP8HXZJnIcBsGGwRi7mwnJYzih95k4bGbltq2xqcUMOjabff+rpq/N57Ymjwe6MtpsNrwNdXy3/g0+2LuIzXY/cT6Ds919OffEX9KvbxdsFqssgNVvw6o3oHC9OWPxiKvM2pvU4epgLK2iUNMMhRoR61hasJRffPELACKCIkgISyAxNJGEsAQSQhOw2WwU1xZTUlfCrqpd5JbnNn72+TOfZ3TKaAD+/MZkPqsr4LOz3+Ht7+7lhfI1hBpwmiuJ03tM5pRh1xAWkdgh17Rx04fMXvUcn1TnUmq3MdDv4NyUsZw1+vau0Tx1KMOAvetg+StmLU5tCaSNNJv7MkaZfXGCQgNdSukiFGqaoVAjYh23fn0rueW5vDb9NcKDwo96/NayrczdMZd0dzrTe08H4L05v+Mv+f/h8rDezKvcxh67wVXubK6b9jRhYfHtfQk/yuupZv6yJ5md8yHf+ssBON0Rzbn9LmDsiF91veYpXwNs/gxWvQk7FkBtKQSFHxwiPvAngS6hdHIKNc1QqBGxhm1l27jgwwv4w6g/cHH2xa06x/qNH3D5oruI9kOVDXrg4OHT/0avnhPatrDHqbhoM58ueaSxeeosZxwPzZgX6GK1nmHAvo2w6TOY/3/grYbkIZB1ltkPJ2WYmqjkMB02T82DDz6IzWbjlltuadz37LPPMmHCBCIjI7HZbJSVlR33OQEmTJiAzWZrsv3yl788nuKLSBf0yLJHSItI4/x+57f6HOvz5lNvs1HosDHGGcUrP53T6QINQFx8fy6f9jRjwjKwGQZn9pwW6CIdH5sNEgfAqbfBrF3w01cgIRsWPw3PToB/jIXP/gAFawNdUumiWj2ke+nSpTzzzDMMHTq0yf6amhqmTp3K1KlTmTVrVpuc84DrrruOe++9t/F1WFhYywsuIp1Wg78Br99LiCPkkI61B60rXsf8XfN54NQHcDlcrf6ei854lLNqiigt3UZqyshDOv52MobBP967iFfrdjArdRKTxs4MdInajt0Og84zN58XtvzHnMF43b9h8T8grq85RHzoz6HHKVqHSo5Jq/5KqqqqmDFjBs899xz33Xdfk/cO1LDMmzevzc55QFhYGMnJya0psoh0cj6/jxmfzmB98XocNgdhQWFEBEUQHhROeFA4EUER7K7aTWZkJlN7Hv/IoLCw+ID2nTkaw+/nqfcv4unqLfw68RQuPfNvgS5S+3EEQfZ0c/PWQc4c2DYPNn4KK/4FIdHmCuN9zzD74KiTsfyIVv3z5KabbmL69OlMnjy5zQpyLOd87bXXiI+PZ/DgwcyaNYuampofPdbj8VBRUdFkE5HO65PcT1hfvJ7fj/w9d46+k2uHXMs5fc5hdMpoekb2JCwojNSIVGaeNBOn3dr/ajf8fp7YH2huSRzL9dOeCXSROk5QCAw4B6Y/Areug+u+MldR37cR/n09PDoAPr4Vtn8HDZ5Al1Y6mRb/l+HNN99k+fLlLF26tM0KcSznvPTSS8nMzCQ1NZXVq1czc+ZMNm3axPvvv9/s8Q888AD33HNPm5VRRNpPXUMdjy9/nDMyz+CKQVcEujgBZfj9PPb+hbxYncPvEsdx1bR/BLpIbc8wYPu3EN8f3EeofbfbIW2EuU38I+zbBCtfg7Xvww8vgivSHEHVcxz0maiZjKVloSYvL4/f/va3zJkzh5CQthlWeKznvP766xufDxkyhJSUFCZNmsTWrVvp06fPYcfPmjWL2267rfF1RUUFGRkZbVJmESup99XjtDux2wLXr+RfG/5FcV0xtwy/JWBl6Cxe+/xXvFidw+8TT+WKaU8FujjHpMFbh8dThqe+Ck9dOXX1lXg8FXi8NXi8VdTVV1PvraGuoQaPt4a6ijzs2xdwQVUVroSB5szDfU43568JPsIQ/YQsOONemPQX2LPCXL9r/YfwwwsQFAYnXAwn3wyxvTWKqptqUahZtmwZhYWFDB8+vHGfz+dj/vz5PPnkk3g8HhwOR4sK0Npzjh5tTp6Vk5PTbKhxuVy4XK3vSCjSHczZMYc75t9Bg7+hse/Kgf4rBx4P7dsSERxBuDOc8ODww/q7HHjucria7eT7Y4pri3l+zfNcnHUxPSJ7tOPVdg1JkT1g3wI83qpWn8PrraG2ppi6unJq60qprSulrr6S2vpKM1x4q/F4a6lrqKHeV0ddQx2eBg8eXx0evxePr546v9d8bjTg8TdQZ/ipx0edYeDBj8cGHsBjg4YWBoggw8AbH0vawAsZ7/GZ63At+js4giFjtBlw+kyE5BOaXyjz0BqcM+6FinxY8Zp5jh9ehPBEc5K/nuNg2AwI0TQe3UWLQs2kSZNYs2ZNk31XX3012dnZzJw5s8WB5njOuXLlSgBSUlKafV9Ejqy2oZaHljzEyKSRTOk5hWpvNdXeaqq8VU2eF9UWUeWtosZb0/iez/D96HmdNifhweFHDD+HhqUF+Quw2+zcMPSGDrz6zuuMcXfyq6K1PF66gsr3LiDcGU5dQy21Pg91Pg81Pg+1fi91RgO1/gbq8FFr+KnFoM4GtS0IGTbDIMQAF+AywIUNl81OCHZcNoe52Z1EOF247EG4HMGE2INxOYJxOV24HKGEOF24nGG4nKG4gsIICQojOCiCkOBwXMFuQoIjCHZFEuKKxBUcSbArEr8Nhv9zOPv6nAb9LzSbo4q2wLavYetX8O2j8OW9EBoLvU87WJMT/SOhNzIVTvu9uWL4zkXmOlR5S2DOn2Heg+Y8OIPOM4OSI6jN/reSzqdFocbtdjN48OAm+8LDw4mLi2vcX1BQQEFBATk5OQCsWbMGt9tNjx49iI2NBcwgc/7553PzzTcf0zm3bt3K66+/zllnnUVcXByrV6/m1ltvZfz48T86/FtEjuyVda9QXFfMi1NebFENiWEYeHyexoDTGHjqqw4LP4cGpDJPGflV+ea++v2fa6jBho0/jv4j0SHR7XexXcwvf/Ivit4+i7cqNhMChBo2QrERanMQYnMQag8iyhFCUlAwoY5gQh0uQp2hhDrDCHGGEhocTmhQBCHBbkJdbkKD3YS6onG53IS4onC5IglxReMMCg3IcHY7EBMSQ1FtkbnDZoOE/uY2+gZoqIfdP5gBZ+vX8PEtYPjNYd4HAk7PUw+vgQmJhP5nmhuYNTgL/w45X8Kq181RVP2nmCGnz0TV4FhQmw8hePrpp5t00B0/fjwAL730EldddRVghpSioqJjPmdwcDBz587lscceo7q6moyMDC688ELuuuuuNi27SHdRWFPIi2tfZEb2jBY3+dhsNkKcIYQ4Q4gPPb4h0X7Dj8fnIdSpIbqHstnt/Oniz/lToAvSjuJD4w+Gmv/mDDb712SeAhPvMpdWyP3WDDk5c2Dpc2BzQPpJB5uqUocfPpdNZCpMuR/OvA/yV8CmT81h4qvfMj+fNtwMSQN/AkmD1Q/HArRMgkg3dNd3dzF/13w+vuBjIoP1/wfpeDfMuYHwoHAenfBoyz9css2swdn2NeTOh7pycEVBr1Oh9wQz5Byps3DpdvPzud+Yj3Vl5vEDzzObqZKHKuB0Ii35/bb2ZA8icph1xeuYvXU2d42+S4FGAiY+NJ68yrzWfTi2t7mddI25YOaelQebqj7/A/gbzP43vffX4vQaD2GxBz8f0xNGXm1uPq8ZbtZ9AMtegu8ehZheZhNV7wkHm7KkS1BNjUg3YhgGV31+FRX1FbxzzjuWn8ROOq9Hlz3K3B1z+fSCT9v2xJ5K2L7ADDnbvoaizYANUk882FSVPsps4vpvPq85f866DyBnLlTshowxMPwKyD4LQmPatqxyTFRTIyLN+v3837O8cDmnZ5zOisIV9I3uS0yI/kMtHS8+5Ah9ao6Hyw1ZU80NoHzXwaaqZS/Dt49AUDj0HHuwJichy2xucgSZr/tMNEdkbf7c7Gg8+yb40GYON+93prklDVITVSekmhqRbuSCDy9gS+kWnDYnDUYDAHEhcfSN7kuf6D70ie5Dr6hepEakkhiWSJBdw1+lfXy67VNmfjuTxZcuJiyogxYn9vth75qDTVU7F4HPA+6Ug6Oqek+AiMSmn6vIh81fwJb9a1J5qyEy3VyPqt+Z5rDzI00aKMelJb/fCjUi3YxhGDT4G9hRsYOc8hy2lm1la9lWtpRuIa8yr3EOGrvNTmJYIrEhsbiD3biD3LiD3UQERxz2OjI4koig/fuD3YQHhatpS45oyZ4lXPOfa/jk/E8CN+lifQ3sXLi/qWoe7F1r7k8aAn0mmEEn85SmC2g2eMx1p7b8xww6pbnmpIE9x5kLcg48D8I770KpXZFCTTMUakSOrt5Xz66qXeyp2kN+dT57qvZQ6imlqr6KyvpKKr2VVNZXNr6u89X96LlCnaGNE+1FBEU0TsR3IPzEhcYRFxJHbEjsweehsbgcmgm8O9hWto1zZ5/LK1NfYXjS8KN/oCNU7jXDzbavzZqcqgJwuCDz5INNVUmDm85yXJQDW74wQ07ut2D4zNFTB/rvZIwxF+mUVlOoaYZCjUjb8/q8VHormw09Vd6qJhPtHfq6vL6ckroSyj3lh53THewmPSKdDHcGPSJ7kOHOaNwSwxIDukaVtJ1yTznj3hzHI6c9wpk9O+EII8MwVwY/0FS1YwF4ayAsfv+w8dPNoBOVdvAzlXsPdlDe+jVUF4IzZP9yDZdCn0ladLMV1FFYRDpEkCOIWEcssSGxRz+4GV6fl5K6EkrqSiiuK6a4tph9tfvIq8wjrzKP1dtWs7d6Lwbmv70igiLIis1iQOwABsQNYEDsAHpF9VJTVxcUGRxJkD2ofToLtwWbDRIHmNvJN5nNTnmLD3Y6XvseYEB81sGA03McDLvE3AwD9q4zj10/G979hTnhX4+TD/bFSRygzsZtTDU1ItKpeXwedlftJq8ijy1lW9hQvIGNJRvZWbkTAJfDRf+Y/gyIHUB2XDYDYwfSN6avmrG6gDPfPZOze5/Nb4b/JtBFabmakqZNVeV5YA8yF9I80FSVOgzs+9cvLMszm6i2zDHnxfHWQGTa/maqSWbtT1jr/nFgdWp+aoZCjYi1VNZXsrFkIxtLNrKheAMbSjawrXwbfsOP0+akd3RvsmOzGRg3kOzYbLJjswkP0giVzuTijy8mOzabv5zyl0AX5fgYBhRvPRhwcudDfaW51lSv8Qf718T0NI/31sGO7yDnK9j6pdnMZbND8hBz63uGWZujEVWAmp9EpBtwB7s5KfkkTko+qXFfbUMtW0q3sLFkI+uL17OxZCOf5X6G1+/Fho0ekT0am66yY7MZEDtA8/QEUKgzlNqG2kAX4/jZbBDf19xGXWdO4rd72cGmqk9uNzsQx/Q62FTVazz0nWx+/sBcOjsXwe4VsOJf4Aw1g1DfiQeXfZCjUk2NiFia1+9lW9k2NpRsaGy62liykZqGGgCSw5PJjs0mKyaL/jH9yYrNIsOdoQ7JHeDGuTficrh47PTHAl2U9lVXbo6M2va12ZG4ZJtZM5M24mBTVfpIc/I/MN9fP9tsqspbbC77ENPz4MSAPU/tVh2O1fzUDIUaETnAb/jZWbHTDDolG9hYvJFNpZsoqSsBzBqEfjH9zJATk0VWbBb9ovsRERwR4JJby61f30ptQy1Pn/F0oIvSsUp3HGyq2jbPXFAz2G12ND7QVBXX16wB8lSa8+Js/crcinP2B6KR5qR/6aPMQGTh/jgKNc1QqBGRoymqLWJzyWY2lW5ic6n5mFuW2zj7clpEmlmjE7s/7MRkkeZOU61OK836dhZ7qvfw8tSXA12UwPH79i/IuT/g7FwEfq85Y3GfCfsX5JwA4XHm8Y2B6Ctzjaua/aPHkodC/ynmqKq0EQc7KFuAQk0zFGpEpDXqffXklueyqXQTm0rMsLO5dHNjrU6YM4x+Mf0aa3T6x/SnX0w/dUo+BvcsvId1Ret4+5y3A12UzsNTBTu+Pxhc9m0EbJAy9OBSDgcm9DMMc0bjnYvNBThz5pq1PmFxZn+dvpObX/ahi1GoaYZCjYi0FcMwzFqd/bU5B8JObnlu4zITGe6Mxuar/rH96R/Tn/SIdGyal6TR35b/jU+3fcoXF30R6KJ0XhX5Zg3OgU7H1fvMWY57jIZep5lb6ongcIKvweygvOUL2Pwfc50rOLjsQ5+J5jw5hy770AUo1DRDoUZE2lu9r56tZVsbw86BpqwyTxkA4UHh9I/p39ghOSsmi77RfTtuQcdO5tV1r/LkyidZMmNJoIvSNfj9ULjenOcmd77Z/FRfub8/ztj9IWc8JA40l3KoKtwfiL46uOyDM8QMNgf67iQOarrsQyekUNMMhRoRCQTDMNhXu49NJZuaBJ3tFdvxG/7GoeaHdkrOiskiOTzZ8rU6H239iDu/u5MlM5YQ6uxatQedgq8B8ldA7jwz5OxcbK46HhZvhpte480FOeP7m8cfuuzD9u+goRbCE/Yv+zDRbN6KTAnkFTVLoaYZCjUi0pnUNdSxtXxrY8g5EHoq6ysBcx6eA0HnwJIQvaN7E2QPCnDJ286C3Qv45dxf8sWFX5AakRro4nR93lrIW3KwJmf3cnN+nNAYsx9Oj9HmY+qJ5siqvMUHQ86eVYABCQMO1uJkntIpJgBUqGmGQo2IdHaGYVBQXdCkr86m0k3sqNgBQLA92FwSIm4AA+MGMiBuAP2i+xHsCA5wyVtnffF6fv7xz3lz+psMih8U6OJYj6cSdi01a3DyFsGuH6C+ChzBkDIMeowxt4zRgM2s8dm6f6h5xS7zuIzRBycMTBkWkKYqhZpmKNSISFdVVV/FptJNrC9e3+ySEH1j+jZZ5LN/TP8u0U+noLqAM949g6cmPcWp6acGujjW52uAwnXmsPGdi8yamord5ntxfc1anLQTIXOcOSS8sanqWzMMhcaac+P0Pt0cOt5BTVVaJkFExEIigiMYkTSCEUkjGvfVNtSyuXRzY8jZULyBj7Z9RIO/AbvNTrQrGrvNjh07NpvNfL5/Pp0Dz20c3G+z2Rpf//f+A+dobr/dZgcbjc8P3X/ge23YDpbhkPL4/OZIsVJPaUDua7fjcELKCeY2+gZzX1meGW52LjKXZ1j5LwgKh2vnQtFmyJoKUx+Ayj37a3G+go9vAcNvTgA44GzIPsdcIqITUKgREemCQp2hnJBwAicknNC4r95XT05ZDuuL11NcW4yBgWEY+PHjN/zmc8N/cL/hx8/B/U3eO8L+o53La3gbnx/tXMMShtEnuk8A72Q3F50BUelmTYzNBlE9zJmNnzsdXG744UUzwCRkw9CfwSVvgN1pLuGw8SP45mGY+xfz/eyzYcyNEB4fsMtR85OIiEh3VVMCH/4aNn4MA35ihputX8FJ18GZ/wMNdeaw8E2fw/oPzNcRSeaIqvh+5mPFbvj+CfN8Ux80g00bUp+aZijUiIiIHCJ3Prx/A3hr4ISLYf2H5jDvc/8O2dMPP7662JwAsGiL2TRVtNkcJu5vMCcEHHAOTHv44JIObUR9akRERKR5DfXw9f2w4G/mRHyxvWHxM2az0/nPQFRa858Lj4MhFzXdV1Ni9reJ7WMu3RBgCjUiIiLdRfFWeO8aKFgDI6+GPath1Rsw8Y8w7raWL4QZFtupVghXqBEREbE6w4CVr8Onvwd3ktnvZdmrEBIFv/gcMkYFuoRtQqFGRETEyurK4eNbYe17MPA8c5TT90/AoAvg7L9CaHSgS9hmFGpERESsKm+J2dxUWwZjfwsbPzVHK/3kSTjxMjPgWIhCjYiIiNX4ffDtIzDvQUgbDllnwaJ/QEIW3DDfHI5tQQo1IiIiVlK+C96/HnYuhOFXQul2WPw0jLkJJt8NTlegS9huFGpERESsYv2H5mR6weHmnDHfPGzOCDzjXeh3RqBL1+4UakRERLq62Tebw7T3rDQnwTvncVjynDmx3q+XmyOeugGFGhERka5uzTvmEgbnPA7DrzA7AIdGg88LEYmBLl2HUagRERHp6qLSIWsajLjy4L6wOPB5oL4aXBGBK1sHsge6ACIiInKcnKHgrWu678BMvzXFHV+eAFGoERER6eqCQsFb23Rf6P5QU1vS8eUJEIUaERGRri4oxOwUfKiw/atlq6ZGREREuoygMLOj8KEam59KO748AaJQIyIi0tU5m6mpCQoz96umRkRERLqMoLDDOwrbbGYTlEKNiIiIdBnN9akBs7OwOgqLiIhIlxEUdvjoJzD71aimRkRERLqMoNDDOwqDQo2IiIh0MU4XlOfB7mVN+9aExXWr0U9aJkFERKSrS8g2H5+bCHYnJAyAlBNg48dQVxbQonUkhRoREZGubsA58McC2LveXKl7zypzOxBo6sohJCqQJewQCjUiIiJWEBQK6SPM7YCGenP0UzcINKA+NSIiItblDAZ3cqBL0WEUakRERMQSFGpERETEEhRqRERExBIUakRERMQSFGpERETEEhRqRERExBIUakRERMQSFGpERETEEhRqRERExBIUakRERMQSFGpERETEEhRqRERExBIUakRERMQSnIEuQEcxDAOAioqKAJdEREREjtWB3+0Dv+NH0m1CTWVlJQAZGRkBLomIiIi0VGVlJVFRUUc8xmYcS/SxAL/fT35+Pm63G5vNRkVFBRkZGeTl5REZGRno4nUJumctp3vWcrpnLaP71XK6Zy0XyHtmGAaVlZWkpqZitx+510y3qamx2+2kp6cftj8yMlJ/1C2ke9Zyumctp3vWMrpfLad71nKBumdHq6E5QB2FRURExBIUakRERMQSum2ocblc3H333bhcrkAXpcvQPWs53bOW0z1rGd2vltM9a7mucs+6TUdhERERsbZuW1MjIiIi1qJQIyIiIpagUCMiIiKWoFAjIiIilmCZULN582bOPfdc4uPjiYyMZNy4cXz99deN769atYpLLrmEjIwMQkNDGTBgAH/729+OeM558+Zhs9ma3ZYuXXrY8Tk5ObjdbqKjo9v68tpFoO7ZvHnzOPfcc0lJSSE8PJxhw4bx2muvteu1tpVA/p2tXr2aU089lZCQEDIyMnj44Yfb7TrbSnvcrwM++eQTRo8eTWhoKDExMZx33nlN3l+6dCmTJk0iOjqamJgYpkyZwqpVq9ry8tpFIO8ZwMsvv8zQoUMJCQkhMTGRm266qa0urd0E+p4BFBcXk56ejs1mo6ysrA2uqn0F6p4dz3mPiWER/fr1M8466yxj1apVxubNm41f/epXRlhYmLFnzx7DMAzjhRdeMH7zm98Y8+bNM7Zu3Wr885//NEJDQ40nnnjiR8/p8XiMPXv2NNmuvfZao1evXobf729ybH19vTFy5Ehj2rRpRlRUVHteapsJ1D27//77jbvuustYsGCBkZOTYzz22GOG3W43Pvroow657uMRqHtWXl5uJCUlGTNmzDDWrl1rvPHGG0ZoaKjxzDPPdMh1t1Z73C/DMIx3333XiImJMf7xj38YmzZtMtatW2e89dZbje9XVlYasbGxxlVXXWVs3LjRWLt2rXHhhRcaSUlJRn19fbte8/EK1D0zDMN45JFHjNTUVOO1114zcnJyjFWrVhmzZ89ut2ttK4G8Zwece+65xrRp0wzAKC0tbetLbHOBumetPe+xskSo2bdvnwEY8+fPb9xXUVFhAMacOXN+9HO/+tWvjNNPP/2Yv6e+vt5ISEgw7r333sPeu+OOO4zLLrvMeOmll7pEqOkM9+xQZ511lnH11Vcf83kDIZD37KmnnjJiYmIMj8fTuG/mzJlGVlZWC6+i47TX/fJ6vUZaWprx/PPP/+gxS5cuNQBj586djftWr15tAMaWLVtaeCUdJ5D3rKSkxAgNDTXmzp3busIHSCDv2QFPPfWUcdpppxlffvlllwg1neGeteS8LWGJ5qe4uDiysrJ49dVXqa6upqGhgWeeeYbExERGjBjxo58rLy8nNjb2mL/nww8/pLi4mKuvvrrJ/q+++op33nmHv//9762+ho4W6Ht2vOcNhEDes4ULFzJ+/HiCg4Mb902ZMoVNmzZRWlraugtqZ+11v5YvX87u3bux2+2ceOKJpKSkMG3aNNauXdt4TFZWFnFxcbzwwgvU19dTW1vLCy+8wIABA+jZs2dbXmabCuQ9mzNnDn6/n927dzNgwADS09P52c9+Rl5eXpteY1sL5D0DWL9+Pffeey+vvvrqURdb7CwCfc9aet4WaZNo1Ank5eUZI0aMMGw2m+FwOIyUlBRj+fLlP3r8ggULDKfTaXzxxRfH/B3Tpk0zpk2b1mRfUVGRkZGRYXzzzTeGYRhdpqbGMAJ3z/7bW2+9ZQQHBxtr16495vMGSqDu2RlnnGFcf/31TfatW7fOAIz169e37CI6UHvcrzfeeMMAjB49ehjvvvuu8cMPPxiXXHKJERcXZxQXFzcet2bNGqNPnz6G3W437Ha7kZWVZWzfvr1Nr689BOqePfDAA0ZQUJCRlZVlfP7558bChQuNSZMmGVlZWU1qCDujQN2zuro6Y+jQocY///lPwzAM4+uvv+4SNTWGEdj/b7b0vC3RqUPNzJkzDeCI24YNGwy/32/85Cc/MaZNm2Z89913xrJly4wbb7zRSEtLM/Lz8w8775o1a4z4+Hjjf/7nf465LHl5eYbdbjfefffdJvvPP/98Y+bMmY2vAx1qusI9O9RXX31lhIWFGa+88kqrrrctdIV71plCTaDv12uvvWYATfoT1dXVGfHx8cbTTz9tGIZh1NTUGKNGjTKuuOIKY8mSJcbChQuNCy+80Bg0aJBRU1PTtjfkGHSFe3b//fcbQJMfl8LCQsNutxuff/55G92JY9cV7tmtt95q/PznP298P9Chpivcs9actyU6dagpLCw0NmzYcMTN4/EYc+fONex2u1FeXt7k83379jUeeOCBJvvWrVtnJCYmGnfeeWeLynLvvfcaCQkJh3UyjIqKMhwOR+Nmt9sNwHA4HMYLL7zQugs/Dl3hnh0wb948Izw8POCdXbvCPbv88suNc889t8m+r776ygCMkpKSFn3H8Qr0/Tpw3d9++22T/aNGjWr8/PPPP28kJiYaPp+v8X2Px2OEhYUZb7zxRmsvvdW6wj178cUXDcDIy8trckxiYqLx7LPPtuayj0tXuGcnnHCCYbfbm/3v/5///OfjvAMt1xXuWWvO2xLOVrVZdZCEhAQSEhKOelxNTQ3AYe2Zdrsdv9/f+HrdunVMnDiRK6+8kvvvv/+Yy2EYBi+99BJXXHEFQUFBTd5buHAhPp+v8fXs2bN56KGH+P7770lLSzvm72grXeGegTmM+eyzz+ahhx7i+uuvP+bztoeucM9OPvlk/vjHP+L1ehvfmzNnDllZWcTExBzzd7SFQN+vESNG4HK52LRpE+PGjQPA6/Wyfft2MjMzG7/bbrdjs9mafK/NZmvy3R2lK9yzsWPHArBp0ybS09MBKCkpoaioqPGYjtQV7tl7771HbW1t42eWLl3KL37xC7799lv69Olz9ItsY13hnrXmvC3SphEpQPbt22fExcUZF1xwgbFy5Upj06ZNxu23324EBQUZK1euNAzDrOZKSEgwLrvssiZDZwsLCxvPs3jxYiMrK8vYtWtXk/PPnTu3sdruaALd/HSsAnnPDjQ5zZo1q8l5f6zNtbMI5D0rKyszkpKSjMsvv9xYu3at8eabbxphYWEBr+U6kva8X7/97W+NtLQ044svvjA2btxoXHPNNUZiYmJjrdWGDRsMl8tl3Hjjjcb69euNtWvXGpdddpkRFRXVbPV6ZxHIe2YY5rDkQYMGGQsWLDDWrFljnH322cbAgQM79TD4QN+zQwW6+elYBfKeHct5j4clQo1hmEM4zzzzTCM2NtZwu93GmDFjjE8//bTx/bvvvrvZ9sXMzMzGYw78Qebm5jY59yWXXGKccsopx1SOrhJqDCNw9+zKK69s9rynnXZaO1xl2wrk39mqVauMcePGGS6Xy0hLSzMefPDBtr68Ntde96u+vt743e9+ZyQmJhput9uYPHnyYR3N//Of/xhjx441oqKijJiYGGPixInGwoUL2/uSj1sg71l5ebnxi1/8woiOjjZiY2ON888/v8mw+M4qkPfsUF0l1BhG4O7ZsZz3eNgMwzDarNpHREREJEC6xqB6ERERkaNQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS1CoEREREUtQqBERERFLUKgRERERS/j/KJDlmvQrxQMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for v in [3739,3622,3627]:\n",
    "    plotPoly(tractGeom[v])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bd772b18-8303-457c-8551-5ecd60515903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "manually connecting Kelleys Island bg to mainland\n"
     ]
    }
   ],
   "source": [
    "######## only if we did not read in alt topology ##########\n",
    "print(\"manually connecting Kelleys Island bg to mainland\")\n",
    "Kelley, Knbrs = 3739, [3622, 3627] #FILL THIS IN BASED ON ABOVE; for bgs, use 5879, [5870, 5871]\n",
    "for v in Knbrs:\n",
    "    vtdNbrs[Kelley].append(v)\n",
    "    vtdNbrs[v].append(Kelley)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dc46e2bd-4f0f-4e4c-a472-854aa118b5b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "926 [927]\n",
      "1006 []\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPoly(tractGeom[1006])\n",
    "plotPoly(tractGeom[926])\n",
    "for v in [926,1006]:\n",
    "    print(v,vtdNbrs[v])\n",
    "for v in countyTractList[61]:\n",
    "    plotPoly(tractGeom[v],0.1)\n",
    "    plotCenter(v,tractGeom[v],7)\n",
    "plt.show()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0e0cefa1-dad2-471e-ac40-51655a0eb89a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we also need to connect Catawba to Put-in-Bay\n"
     ]
    }
   ],
   "source": [
    "print(\"we also need to connect Catawba to Put-in-Bay\")\n",
    "vtdNbrs[1006].append(926)\n",
    "vtdNbrs[926].append(1006)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7c421876-68a9-4c3b-a48c-4b58a9cd8efc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets store these vtd lists (with island connections) to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"lets store these vtd lists (with island connections) to a file\")\n",
    "vNo = [v for v in range(nVTDs)]\n",
    "vtdNbrDF = pd.DataFrame( {\"vtdNo\":vNo, \"tractPop\":tractPop, \"centroid x\":tractCPx, \"centroid y\":tractCPy, \"vtdNbrs\":vtdNbrs} )\n",
    "outname = STATE+\"vtdNbrsWithIslandNbrs.csv\"\n",
    "outpath = \"state_map_files/\"+outname\n",
    "vtdNbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eccac5a2-f8c1-470a-8740-e3d191a4b8f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "bgNbrs = [vtdNbrs[v].copy() for v in range(nVTDs)] #nomenclature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6feefb20-60a0-4e80-9907-75dcfe87a526",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check - all vtd assigned?\n",
      "8941 8941\n"
     ]
    }
   ],
   "source": [
    "print(\"check - all vtd assigned?\")\n",
    "print(len(tractPop), len(skipList) + np.sum([len(muniTractList[uu]) for uu in range(nCOIs) ]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "893dc6cb-beed-4ace-9dc5-8b2221b67ddb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rebuild the COIgeoms\n",
      "working on COI geom 0\n",
      "working on COI geom 200\n",
      "working on COI geom 400\n",
      "working on COI geom 600\n",
      "all geoms reconstituted. Total area = 11.327 vs map area 11.327\n"
     ]
    }
   ],
   "source": [
    "print(\"rebuild the COIgeoms\")\n",
    "COIgeom = list()\n",
    "for c in range(nCOIs):\n",
    "    if c%200 == 0:\n",
    "        print(\"working on COI geom\",c)\n",
    "    geo = tractGeom[muniTractList[c][0]]\n",
    "    for v in muniTractList[c][1:] :\n",
    "        geo = geo.union(tractGeom[v])\n",
    "    COIgeom.append(geo)\n",
    "print(\"all geoms reconstituted. Total area =\",r3(np.sum([COIgeom[c].area for c in range(nCOIs)])),\"vs map area\",r3(MAP.area) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f1069aa0-8443-4ca1-a502-6683789bb273",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a quick export of the original school shapes as vtd lists, after shedding empties but prior to any splits or fuses\n"
     ]
    }
   ],
   "source": [
    "exportedTractList, exportedPop, exportedCPx, exportedCPy = list(),list(), list(),list()\n",
    "print(\"this is a quick export of the original school shapes as vtd lists, after shedding empties but prior to any splits or fuses\")\n",
    "for u in range(nCOIs):\n",
    "    exportedTractList.append(muniTractList[u])\n",
    "    exportedPop.append(np.sum([tractPop[v] for v in muniTractList[u] ]) )\n",
    "    geo = COIgeom[u] #tractGeom[muniTractList[u][0]]  #we already rebuilt these\n",
    "    #for v in muniTractList[u] :\n",
    "    #    geo = geo.union(tractGeom[v])\n",
    "    exportedCPx.append(geo.centroid.x)\n",
    "    exportedCPy.append(geo.centroid.y)\n",
    "unitNo = [i for i in range(len(exportedPop))]        \n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":exportedCPx,\"centroid y\":exportedCPy,\"unitPop\":exportedPop,\n",
    "                       \"unitTractList\":exportedTractList} )\n",
    "outname = STATE+\"schoolVTDlists.csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3d6e5556-3e5b-4129-b045-7fb24e22f8ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Integrity check: what school district captured the Put-In-Bay VTD?\n",
      "Put-in-Bay COI no 408 with TOTAL pop 813\n",
      "1006 813\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "where are those empty SDs?\n"
     ]
    }
   ],
   "source": [
    "print(\"Integrity check: what school district captured the Put-In-Bay VTD?\")\n",
    "for u in range(nCOIs):\n",
    "    if 1006 in muniTractList[u]:\n",
    "        plotPoly(COIgeom[u],0.1)\n",
    "        plotPoly(tractGeom[1006],2)\n",
    "        print(\"Put-in-Bay COI no\",u,\"with TOTAL pop\",np.sum([tractPop[v] for v in muniTractList[u] ]) )\n",
    "        for v in muniTractList[u]:\n",
    "            plotPoly(tractGeom[v],1)\n",
    "            print(v,tractPop[v])\n",
    "        plotPoly(countyGeom[21])\n",
    "        plotPoly(countyGeom[61])\n",
    "    plt.show()\n",
    "print(\"where are those empty SDs?\")\n",
    "for u in range(nCOIs):\n",
    "    if len(muniTractList[u]) == 0:\n",
    "        plotPoly(COIgeom[u])\n",
    "        plotCenter(u,COIgeom[u])\n",
    "        for c in range(nCounties):\n",
    "            if countyGeom[c].contains(COIgeom[u].centroid) :\n",
    "                plotPoly(countyGeom[c],0.1)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5a92ffd2-6e0d-4878-8a32-5d7a904830b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will splinter any noncontiguous COIs / SDs into their contig parts.  County-straddlers are OK.\n",
      "first, ID the non-rook contig units ...\n",
      "we found a total of 23 Units that were not rook-contiguous\n"
     ]
    }
   ],
   "source": [
    "print(\"we will splinter any noncontiguous COIs / SDs into their contig parts.  County-straddlers are OK.\")\n",
    "print(\"first, ID the non-rook contig units ...\")\n",
    "isContigUnit = [False]*nCOIs\n",
    "nonContigUnits = list()\n",
    "for c in range(nCOIs):\n",
    "    isContigUnit[c] = isContiguous(muniTractList[c],vtdNbrs)[0]\n",
    "    if not isContigUnit[c]:\n",
    "        nonContigUnits.append(c)\n",
    "print(\"we found a total of\",len(nonContigUnits),\"Units that were not rook-contiguous\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c63283ed-3fcb-4e5b-aaf0-237fd1a8b891",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "split out these noncontig COIs, like fragmented cities\n",
      "after splitting the 23 queen-adj units, we have 642 units\n",
      "check - all vtds assigned?\n",
      "8941 8941\n"
     ]
    }
   ],
   "source": [
    "print(\"split out these noncontig COIs, like fragmented cities\")\n",
    "for u in nonContigUnits:\n",
    "    nVTDsAssigned, nVTDsAvailable = 0, len(muniTractList[u])\n",
    "    contigList = getContigFromStarter(muniTractList[u][0],muniTractList[u],vtdNbrs)\n",
    "    remainList = list(set(muniTractList[u]).difference(set(contigList)) )\n",
    "    muniTractList[u] = contigList.copy()\n",
    "    nVTDsAssigned += len(contigList)\n",
    "    while len(remainList) > 0:\n",
    "        contigList = getContigFromStarter(remainList[0],remainList,vtdNbrs)\n",
    "        muniTractList.append(contigList.copy())\n",
    "        nVTDsAssigned += len(contigList)\n",
    "        remainList = list(set(remainList).difference(set(contigList)) )\n",
    "    if nVTDsAssigned != nVTDsAvailable:\n",
    "        print(\"oops, for unit\",u,\"only\",nVTDsAssigned,\"were assigned out of\",nVTDsAvailable)\n",
    "nCOIs = len(muniTractList)\n",
    "print(\"after splitting the\",len(nonContigUnits),\"queen-adj units, we have\",nCOIs,\"units\")  \n",
    "print(\"check - all vtds assigned?\")\n",
    "print(len(tractPop), len(skipList) + np.sum([len(muniTractList[uu]) for uu in range(nCOIs) ]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8ec542bf-6a14-4c15-a722-96917f993290",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we have a total of 0 units with no vtds and 23 units with 1 vtd\n",
      "Here are the COIs with zero vtds []\n",
      "after eliminating these zeros, we have 642 COIs\n"
     ]
    }
   ],
   "source": [
    "nZero, nOne = 0,0\n",
    "zeroList, oneList = list(), list()\n",
    "for u in range(nCOIs):\n",
    "    if len(muniTractList[u]) == 0:\n",
    "        nZero +=1\n",
    "        zeroList.append(u)\n",
    "    if len(muniTractList[u]) == 1:\n",
    "        nOne +=1\n",
    "        oneList.append(u)\n",
    "print(\"we have a total of\",nZero,\"units with no vtds and\",nOne,\"units with 1 vtd\")\n",
    "print(\"Here are the COIs with zero vtds\",zeroList)\n",
    "\n",
    "oldTractList = [muniTractList[c].copy() for c in range(nCOIs)]\n",
    "muniTractList = list()\n",
    "for c in range(nCOIs):\n",
    "    if len(oldTractList[c]) > 0:\n",
    "        muniTractList.append(oldTractList[c])\n",
    "nCOIs = len(muniTractList)\n",
    "print(\"after eliminating these zeros, we have\",nCOIs,\"COIs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1ba3f04a-9501-42b8-bf36-974502f669cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the min and max district pops for our 15 -district map are 782696 790563\n"
     ]
    }
   ],
   "source": [
    "minDistrictPop = 0.995 * aDP\n",
    "maxDistrictPop = 2.*aDP - minDistrictPop\n",
    "print(\"the min and max district pops for our\",nDistricts,\"-district map are\",int(minDistrictPop),int(maxDistrictPop) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "dbb7b900-8997-44c1-ba12-559a7e071b88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "compute and plot the COI pops by area, ID the big COIs\n"
     ]
    },
    {
     "data": {
      "image/png": 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QIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZcjsIff/993jsscfQr18/BAUFYejQoTh69Ki0XQiBZcuWITIyEkFBQUhJScGZM2ec9lFZWYlp06ZBp9MhJCQEM2bMwNWrV51qjh8/jnvuuQdarRbR0dFYuXJls7Zs3rwZgwcPhlarxdChQ7Fjxw6n7a60hYiIiOTLrSB0+fJljBo1CoGBgfj8889RVFSE119/HX379pVqVq5ciVWrVmHdunU4fPgwevXqhdTUVNTU1Eg106ZNw8mTJ5GdnY3t27dj3759mDVrlrTdarVi3LhxGDBgAPLy8vDqq6/ipZdewrvvvivVHDx4EFOnTsWMGTNw7NgxTJw4ERMnTkRhYaFbbSEiIiIZE25YvHixGD16dKvb7Xa7MBgM4tVXX5Veq6qqEhqNRnz44YdCCCGKiooEAHHkyBGp5vPPPxcKhUJ8//33Qggh3n77bdG3b19RW1vr9Nm33nqr9PyRRx4RaWlpTp+fnJwsnn76aZfb0h6LxSIACIvF4lI9ERER+Z47x2+3RoQ+/fRTjBw5Er/4xS8QHh6OO+64A3/961+l7SUlJTCbzUhJSZFe0+v1SE5OhslkAgCYTCaEhIRg5MiRUk1KSgqUSiUOHz4s1dx7771Qq9VSTWpqKk6fPo3Lly9LNY0/x1Hj+BxX2tJUbW0trFar04OIiIh6LreC0Lfffou1a9di0KBB2LlzJ+bMmYP/+Z//wYYNGwAAZrMZABAREeH0voiICGmb2WxGeHi40/aAgACEhoY61bS0j8af0VpN4+3ttaWpjIwM6PV66REdHd3el4SIiIi6MbeCkN1ux4gRI/C///u/uOOOOzBr1izMnDkT69at81b7utTSpUthsVikx/nz533dJCIiIvIit4JQZGQk4uPjnV677bbbUFpaCgAwGAwAgPLycqea8vJyaZvBYEBFRYXT9oaGBlRWVjrVtLSPxp/RWk3j7e21pSmNRgOdTuf0ICIiop7LrSA0atQonD592um1r7/+GgMGDAAAxMXFwWAwICcnR9putVpx+PBhGI1GAIDRaERVVRXy8vKkml27dsFutyM5OVmq2bdvH+rr66Wa7Oxs3HrrrdIVakaj0elzHDWOz3GlLURERCRz7szCzs3NFQEBAeJPf/qTOHPmjHj//fdFcHCweO+996Sal19+WYSEhIitW7eK48ePi4ceekjExcWJ6upqqWb8+PHijjvuEIcPHxb79+8XgwYNElOnTpW2V1VViYiICPH444+LwsJCsXHjRhEcHCzeeecdqebAgQMiICBAvPbaa6K4uFgsX75cBAYGihMnTrjVlrbwqjEiIqLux53jt1tBSAghtm3bJhISEoRGoxGDBw8W7777rtN2u90uXnzxRRERESE0Go0YO3asOH36tFPNpUuXxNSpU0Xv3r2FTqcTTzzxhLhy5YpTzVdffSVGjx4tNBqNuPHGG8XLL7/crC0fffSRuOWWW4RarRZDhgwRn332mdttaQuDEBERUffjzvFbIYQQvh2T8l9WqxV6vR4Wi4XzhYiIiLoJd47fvNcYERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREclWgK8bQOSPbHaB3JJKVFypQXgfLZLiQqFSKnzdLCIi8jAGIaImsgrLsGJbEcosNdJrkXotlk+Ix/iESB+2jIiIPI2nxogaySosw5z38p1CEACYLTWY814+sgrLfNQyIiLyBgYhov+w2QVWbCuCaGGb47UV24pgs7dUQURE3RGDENF/5JZUNhsJakwAKLPUILeksusaRUREXsUgRPQfFVdaD0EdqSMiIv/HIET0H+F9tB6tIyIi/8cgRPQfSXGhiNRr0dpF8gpcv3osKS60K5tFRERexCBE9B8qpQLLJ8QDQLMw5Hi+fEI81xMiIupBGISIGhmfEIm1j42AQe98+sug12LtYyO4jhARUQ/DBRWJmhifEIkH4g1cWZqISAYYhIhaoFIqYLy5n6+bQUREXsZTY0RERCRbDEJEREQkWwxCREREJFtuBaGXXnoJCoXC6TF48GBpe01NDdLT09GvXz/07t0bkydPRnl5udM+SktLkZaWhuDgYISHh2PRokVoaGhwqtmzZw9GjBgBjUaDgQMHIjMzs1lb1qxZg9jYWGi1WiQnJyM3N9dpuyttISIiInlze0RoyJAhKCsrkx779++Xti1YsADbtm3D5s2bsXfvXly4cAGTJk2StttsNqSlpaGurg4HDx7Ehg0bkJmZiWXLlkk1JSUlSEtLw3333YeCggLMnz8fTz31FHbu3CnVbNq0CQsXLsTy5cuRn5+PYcOGITU1FRUVFS63hYiIiAjCDcuXLxfDhg1rcVtVVZUIDAwUmzdvll4rLi4WAITJZBJCCLFjxw6hVCqF2WyWatauXSt0Op2ora0VQgjx3HPPiSFDhjjte8qUKSI1NVV6npSUJNLT06XnNptNREVFiYyMDJfb4gqLxSIACIvF4vJ7iIiIyLfcOX67PSJ05swZREVF4aabbsK0adNQWloKAMjLy0N9fT1SUlKk2sGDByMmJgYmkwkAYDKZMHToUEREREg1qampsFqtOHnypFTTeB+OGsc+6urqkJeX51SjVCqRkpIi1bjSlpbU1tbCarU6PYiIiKjncisIJScnIzMzE1lZWVi7di1KSkpwzz334MqVKzCbzVCr1QgJCXF6T0REBMxmMwDAbDY7hSDHdse2tmqsViuqq6tx8eJF2Gy2Fmsa76O9trQkIyMDer1eekRHR7v2hSEiIqJuya0FFR988EHp/2+//XYkJydjwIAB+OijjxAUFOTxxnW1pUuXYuHChdJzq9XKMERERNSDdery+ZCQENxyyy345ptvYDAYUFdXh6qqKqea8vJyGAwGAIDBYGh25ZbjeXs1Op0OQUFBCAsLg0qlarGm8T7aa0tLNBoNdDqd04OIiIh6rk4FoatXr+Ls2bOIjIxEYmIiAgMDkZOTI20/ffo0SktLYTQaAQBGoxEnTpxwurorOzsbOp0O8fHxUk3jfThqHPtQq9VITEx0qrHb7cjJyZFqXGkLERERkVtXjT377LNiz549oqSkRBw4cECkpKSIsLAwUVFRIYQQYvbs2SImJkbs2rVLHD16VBiNRmE0GqX3NzQ0iISEBDFu3DhRUFAgsrKyxA033CCWLl0q1Xz77bciODhYLFq0SBQXF4s1a9YIlUolsrKypJqNGzcKjUYjMjMzRVFRkZg1a5YICQlxuhqtvba4gleNERERdT/uHL/dCkJTpkwRkZGRQq1WixtvvFFMmTJFfPPNN9L26upq8cwzz4i+ffuK4OBg8fDDD4uysjKnfZw7d048+OCDIigoSISFhYlnn31W1NfXO9Xs3r1bDB8+XKjVanHTTTeJ9evXN2vL6tWrRUxMjFCr1SIpKUkcOnTIabsrbWkPgxAREVH3487xWyGEEL4dk/JfVqsVer0eFouF84WIiIi6CXeO37zXGBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREckWgxARERHJFoMQERERyRaDEBEREclWp4LQyy+/DIVCgfnz50uv1dTUID09Hf369UPv3r0xefJklJeXO72vtLQUaWlpCA4ORnh4OBYtWoSGhganmj179mDEiBHQaDQYOHAgMjMzm33+mjVrEBsbC61Wi+TkZOTm5jptd6UtREREJF8dDkJHjhzBO++8g9tvv93p9QULFmDbtm3YvHkz9u7diwsXLmDSpEnSdpvNhrS0NNTV1eHgwYPYsGEDMjMzsWzZMqmmpKQEaWlpuO+++1BQUID58+fjqaeews6dO6WaTZs2YeHChVi+fDny8/MxbNgwpKamoqKiwuW2EBERkcyJDrhy5YoYNGiQyM7OFmPGjBHz5s0TQghRVVUlAgMDxebNm6Xa4uJiAUCYTCYhhBA7duwQSqVSmM1mqWbt2rVCp9OJ2tpaIYQQzz33nBgyZIjTZ06ZMkWkpqZKz5OSkkR6err03GaziaioKJGRkeFyW9pjsVgEAGGxWFyqJyIiIt9z5/jdoRGh9PR0pKWlISUlxen1vLw81NfXO70+ePBgxMTEwGQyAQBMJhOGDh2KiIgIqSY1NRVWqxUnT56UapruOzU1VdpHXV0d8vLynGqUSiVSUlKkGlfaQkRERPIW4O4bNm7ciPz8fBw5cqTZNrPZDLVajZCQEKfXIyIiYDabpZrGIcix3bGtrRqr1Yrq6mpcvnwZNputxZpTp0653JamamtrUVtbKz23Wq0t1hEREVHP4NaI0Pnz5zFv3jy8//770Gq13mqTz2RkZECv10uP6OhoXzeJiIiIvMitIJSXl4eKigqMGDECAQEBCAgIwN69e7Fq1SoEBAQgIiICdXV1qKqqcnpfeXk5DAYDAMBgMDS7csvxvL0anU6HoKAghIWFQaVStVjTeB/ttaWppUuXwmKxSI/z58+7/sUhIiKibsetIDR27FicOHECBQUF0mPkyJGYNm2a9P+BgYHIycmR3nP69GmUlpbCaDQCAIxGI06cOOF0dVd2djZ0Oh3i4+Olmsb7cNQ49qFWq5GYmOhUY7fbkZOTI9UkJia225amNBoNdDqd04OIiIh6sM7OzG581ZgQQsyePVvExMSIXbt2iaNHjwqj0SiMRqO0vaGhQSQkJIhx48aJgoICkZWVJW644QaxdOlSqebbb78VwcHBYtGiRaK4uFisWbNGqFQqkZWVJdVs3LhRaDQakZmZKYqKisSsWbNESEiI09Vo7bWlPbxqjIiIqPtx5/jt9mTp9rzxxhtQKpWYPHkyamtrkZqairffflvarlKpsH37dsyZMwdGoxG9evXC9OnT8fvf/16qiYuLw2effYYFCxbgzTffRP/+/fG3v/0NqampUs2UKVPwww8/YNmyZTCbzRg+fDiysrKcJlC31xYiIiKSN4UQQvi6Ef7KarVCr9fDYrHwNBkREVE34c7x2+MjQkSeZLML5JZUouJKDcL7aJEUFwqVUuHrZhERUQ/BIER+K6uwDCu2FaHMUiO9FqnXYvmEeIxPiPRhy4iIqKfg3efJL2UVlmHOe/lOIQgAzJYazHkvH1mFZT5qGRER9SQMQuR3bHaBFduK0NLkNcdrK7YVwWbn9DYiIuocBiHyO7kllc1GghoTAMosNcgtqey6RhERUY/EIER+p+JK6yGoI3VEREStYRAivxPex7X72LlaR0RE1BoGIfI7SXGhiNRr0dpF8gpcv3osKS60K5tFREQ9EIMQ+R2VUoHlE67fd65pGHI8Xz4hnusJERFRpzEIkV8anxCJtY+NgEHvfPrLoNdi7WMjuI4QERF5BBdUJL81PiESD8QbuLI0ERF5DYMQ+TWVUgHjzf183QwiIuqheGqMiIiIZItBiIiIiGSLp8bI7/EO9ERE5C0MQuTXeAd6IiLyJp4aI7/FO9ATEZG3MQiRX+Id6ImIqCswCJFf4h3oiYioKzAIkV/iHeiJiKgrMAiRX+Id6ImIqCswCJFf4h3oiYioKzAIkV/iHeiJiKgrMAiR3+Id6ImIyNu4oCL5Nd6BnoiIvIlBiPwe70BPRETewlNjREREJFsMQkRERCRbDEJEREQkWwxCREREJFsMQkRERCRbDEJEREQkW24FobVr1+L222+HTqeDTqeD0WjE559/Lm2vqalBeno6+vXrh969e2Py5MkoLy932kdpaSnS0tIQHByM8PBwLFq0CA0NDU41e/bswYgRI6DRaDBw4EBkZmY2a8uaNWsQGxsLrVaL5ORk5ObmOm13pS1EREQkb24Fof79++Pll19GXl4ejh49ivvvvx8PPfQQTp48CQBYsGABtm3bhs2bN2Pv3r24cOECJk2aJL3fZrMhLS0NdXV1OHjwIDZs2IDMzEwsW7ZMqikpKUFaWhruu+8+FBQUYP78+Xjqqaewc+dOqWbTpk1YuHAhli9fjvz8fAwbNgypqamoqKiQatprCxERERFEJ/Xt21f87W9/E1VVVSIwMFBs3rxZ2lZcXCwACJPJJIQQYseOHUKpVAqz2SzVrF27Vuh0OlFbWyuEEOK5554TQ4YMcfqMKVOmiNTUVOl5UlKSSE9Pl57bbDYRFRUlMjIyhBDCpba4wmKxCADCYrG4/B4iIiLyLXeO3x2eI2Sz2bBx40Zcu3YNRqMReXl5qK+vR0pKilQzePBgxMTEwGQyAQBMJhOGDh2KiIgIqSY1NRVWq1UaVTKZTE77cNQ49lFXV4e8vDynGqVSiZSUFKnGlba0pLa2Flar1elBREREPZfbQejEiRPo3bs3NBoNZs+ejU8++QTx8fEwm81Qq9UICQlxqo+IiIDZbAYAmM1mpxDk2O7Y1laN1WpFdXU1Ll68CJvN1mJN432015aWZGRkQK/XS4/o6GjXvihERETULbkdhG699VYUFBTg8OHDmDNnDqZPn46ioiJvtK3LLV26FBaLRXqcP3/e100iIiIiL3L7pqtqtRoDBw4EACQmJuLIkSN48803MWXKFNTV1aGqqsppJKa8vBwGgwEAYDAYml3d5biSq3FN06u7ysvLodPpEBQUBJVKBZVK1WJN432015aWaDQaaDQaN74aRERE1J11eh0hu92O2tpaJCYmIjAwEDk5OdK206dPo7S0FEajEQBgNBpx4sQJp6u7srOzodPpEB8fL9U03oejxrEPtVqNxMREpxq73Y6cnBypxpW2EBEREbl11diSJUvE3r17RUlJiTh+/LhYsmSJUCgU4osvvhBCCDF79mwRExMjdu3aJY4ePSqMRqMwGo3S+xsaGkRCQoIYN26cKCgoEFlZWeKGG24QS5culWq+/fZbERwcLBYtWiSKi4vFmjVrhEqlEllZWVLNxo0bhUajEZmZmaKoqEjMmjVLhISEOF2N1l5bXMGrxoiIiLofd47fbgWhJ598UgwYMECo1Wpxww03iLFjx0ohSAghqqurxTPPPCP69u0rgoODxcMPPyzKysqc9nHu3Dnx4IMPiqCgIBEWFiaeffZZUV9f71Sze/duMXz4cKFWq8VNN90k1q9f36wtq1evFjExMUKtVoukpCRx6NAhp+2utKU9DEJERETdjzvHb4UQQvh2TMp/Wa1W6PV6WCwW6HQ6XzeHiIiIXODO8Zv3GiMiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItkK8HUDqGew2QVySypRcaUG4X20SIoLhUqp8HWziIiI2sQgRJ2WVViGFduKUGapkV6L1GuxfEI8xidE+rBlREREbeOpMeqUrMIyzHkv3ykEAYDZUoM57+Ujq7DMRy0jIiJqH4MQdZjNLrBiWxFEC9scr63YVgSbvaUKIiIi32MQog7LLalsNhLUmABQZqlBbkll1zWKiIjIDW4FoYyMDNx5553o06cPwsPDMXHiRJw+fdqppqamBunp6ejXrx969+6NyZMno7y83KmmtLQUaWlpCA4ORnh4OBYtWoSGhganmj179mDEiBHQaDQYOHAgMjMzm7VnzZo1iI2NhVarRXJyMnJzc91uC3VcxZXWQ1BH6oiIiLqaW0Fo7969SE9Px6FDh5CdnY36+nqMGzcO165dk2oWLFiAbdu2YfPmzdi7dy8uXLiASZMmSdttNhvS0tJQV1eHgwcPYsOGDcjMzMSyZcukmpKSEqSlpeG+++5DQUEB5s+fj6eeego7d+6UajZt2oSFCxdi+fLlyM/Px7Bhw5CamoqKigqX20KdE95H69E6IiKirqYQQnR4AscPP/yA8PBw7N27F/feey8sFgtuuOEGfPDBB/jv//5vAMCpU6dw2223wWQy4a677sLnn3+O//qv/8KFCxcQEREBAFi3bh0WL16MH374AWq1GosXL8Znn32GwsJC6bMeffRRVFVVISsrCwCQnJyMO++8E2+99RYAwG63Izo6Gr/5zW+wZMkSl9rSHqvVCr1eD4vFAp1O19EvU49lswuMfmUXzJaaFucJKQAY9FrsX3w/L6UnIqIu487xu1NzhCwWCwAgNDQUAJCXl4f6+nqkpKRINYMHD0ZMTAxMJhMAwGQyYejQoVIIAoDU1FRYrVacPHlSqmm8D0eNYx91dXXIy8tzqlEqlUhJSZFqXGlLU7W1tbBarU4Pap1KqcDyCfEAroeexhzPl0+IZwgiIiK/1eEgZLfbMX/+fIwaNQoJCQkAALPZDLVajZCQEKfaiIgImM1mqaZxCHJsd2xrq8ZqtaK6uhoXL16EzWZrsabxPtprS1MZGRnQ6/XSIzo62sWvhnyNT4jE2sdGwKB3Pv1l0Gux9rERXEeIiIj8WocXVExPT0dhYSH279/vyfb41NKlS7Fw4ULpudVqZRhywfiESDwQb+DK0kRE1O10KAjNnTsX27dvx759+9C/f3/pdYPBgLq6OlRVVTmNxJSXl8NgMEg1Ta/uclzJ1bim6dVd5eXl0Ol0CAoKgkqlgkqlarGm8T7aa0tTGo0GGo3Gja8EOaiUChhv7ufrZhAREbnFrVNjQgjMnTsXn3zyCXbt2oW4uDin7YmJiQgMDEROTo702unTp1FaWgqj0QgAMBqNOHHihNPVXdnZ2dDpdIiPj5dqGu/DUePYh1qtRmJiolON3W5HTk6OVONKW6hjbHYB09lL2FrwPUxnL3HBRCIi6rbcGhFKT0/HBx98gK1bt6JPnz7SXBu9Xo+goCDo9XrMmDEDCxcuRGhoKHQ6HX7zm9/AaDRKV2mNGzcO8fHxePzxx7Fy5UqYzWa88MILSE9Pl0ZjZs+ejbfeegvPPfccnnzySezatQsfffQRPvvsM6ktCxcuxPTp0zFy5EgkJSXhL3/5C65du4YnnnhCalN7bSH38b5iRETUowg34Ppiwc0e69evl2qqq6vFM888I/r27SuCg4PFww8/LMrKypz2c+7cOfHggw+KoKAgERYWJp599llRX1/vVLN7924xfPhwoVarxU033eT0GQ6rV68WMTExQq1Wi6SkJHHo0CGn7a60pS0Wi0UAEBaLxeX39GSfn7ggYhdvFwOaPGL/8/j8xAVfN5GIiMit43en1hHq6biO0E8cawa1dksNrhlERET+osvWESL54H3FiIioJ2IQIpfwvmJERNQTMQiRS3hfMSIi6okYhMglSXGhiNRrm91Kw0GB61ePJcWFdmWziIiIOoVBiFzC+4oREVFPxCBELuN9xYiIqKfp8L3GSJ54XzEiIupJGITIbbyvGBER9RQ8NUZERESyxSBEREREssUgRERERLLFOULULptdcHI0ERH1SAxC1KaswjKs2FbkdJ+xSL0WyyfE98jL5Rn6iIjkhUGIWpVVWIY57+VDNHndbKnBnPfye9zaQXILfURExDlC1AqbXWDFtqJmIQiA9NqKbUWw2Vuq6H4coa9xCAJ+Cn1ZhWU+ahkREXkTgxC1KLekslkoaEwAKLPUILeksusa5SVyC31ERPQTBiFqUcWV1kNQR+r8mZxCHxEROWMQohaF99G2X+RGnT+TU+gjIiJnDELUoqS4UETqtc3uNO+gwPWJxElxoV3ZLK+QU+gjIiJnDELUIpVSgeUT4gGgWRhyPF8+Ib5HXFoup9BHRETOGISoVeMTIrH2sREw6J1HQgx6bY+6dF5OoY+IiJwphBC8FKYVVqsVer0eFosFOp3O183xGbksMsh1hIiIegZ3jt8MQm1gEJIfuYQ+IqKezJ3jN1eWJmpEpVTAeHM/XzeDiIi6COcIERERkWxxRKiH4ikeIiKi9jEI9UCc9EtEROQanhrrYXjzUCIiItcxCPUgvHkoERGRexiEehBf3jzUZhcwnb2ErQXfw3T2EsMWERF1C5wj1IP46uahnJNERETdFUeEehBf3Dy0K+ckcdSJiIg8ze0gtG/fPkyYMAFRUVFQKBTYsmWL03YhBJYtW4bIyEgEBQUhJSUFZ86ccaqprKzEtGnToNPpEBISghkzZuDq1atONcePH8c999wDrVaL6OhorFy5sllbNm/ejMGDB0Or1WLo0KHYsWOH223pSbr65qFdOScpq7AMo1/Zhal/PYR5Gwsw9a+HMPqVXZz8TUREneJ2ELp27RqGDRuGNWvWtLh95cqVWLVqFdatW4fDhw+jV69eSE1NRU3NTyMG06ZNw8mTJ5GdnY3t27dj3759mDVrlrTdarVi3LhxGDBgAPLy8vDqq6/ipZdewrvvvivVHDx4EFOnTsWMGTNw7NgxTJw4ERMnTkRhYaFbbelJOnvzUHdHXA6dvdQlc5J4JRwREXlLp+41plAo8Mknn2DixIkAro/AREVF4dlnn8Vvf/tbAIDFYkFERAQyMzPx6KOPori4GPHx8Thy5AhGjhwJAMjKysLPfvYz/Pvf/0ZUVBTWrl2L3/3udzCbzVCr1QCAJUuWYMuWLTh16hQAYMqUKbh27Rq2b98uteeuu+7C8OHDsW7dOpfa0p7ueq+xjszZcfc9WYVlWPJ/J1BVXd9ue958dDgeGn5jB3pyPZyNfmVXq4FLAcCg12L/4vu5YCQREQFw7/jt0TlCJSUlMJvNSElJkV7T6/VITk6GyWQCAJhMJoSEhEghCABSUlKgVCpx+PBhqebee++VQhAApKam4vTp07h8+bJU0/hzHDWOz3GlLU3V1tbCarU6Pbqj8QmR2L/4fnw48y68+ehwfDjzLuxffH+bIcidERdHvSshCOjcnCRfXglHREQ9n0eDkNlsBgBEREQ4vR4RESFtM5vNCA8Pd9oeEBCA0NBQp5qW9tH4M1qraby9vbY0lZGRAb1eLz2io6Nd6LV/ctw89L9ujwIAbD9+ocXTXe7O82mrvilPzEny1ZVwREQkD7x8vpGlS5di4cKF0nOr1dqtw5Arp7vcGXEx3tyv3fqm2pqT5ApfXAlHRETy4dERIYPBAAAoLy93er28vFzaZjAYUFFR4bS9oaEBlZWVTjUt7aPxZ7RW03h7e21pSqPRQKfTOT26K1dPd7k74uJqfUhQINY+NqLT6wh19ZVwREQkLx4NQnFxcTAYDMjJyZFes1qtOHz4MIxGIwDAaDSiqqoKeXl5Us2uXbtgt9uRnJws1ezbtw/19T/NQcnOzsatt96Kvn37SjWNP8dR4/gcV9rSU7lzusvdERdX69dM63wIAjp/JRwREVFb3A5CV69eRUFBAQoKCgBcn5RcUFCA0tJSKBQKzJ8/H3/84x/x6aef4sSJE/jVr36FqKgo6cqy2267DePHj8fMmTORm5uLAwcOYO7cuXj00UcRFXV9Pssvf/lLqNVqzJgxAydPnsSmTZvw5ptvOp22mjdvHrKysvD666/j1KlTeOmll3D06FHMnTsXAFxqS0/lzukud0dcXK2/66Z+neiBs/EJkVj72AgY9M4hzKDXemTUiYiI5MvtOUJHjx7FfffdJz13hJPp06cjMzMTzz33HK5du4ZZs2ahqqoKo0ePRlZWFrTanw5i77//PubOnYuxY8dCqVRi8uTJWLVqlbRdr9fjiy++QHp6OhITExEWFoZly5Y5rTV0991344MPPsALL7yA559/HoMGDcKWLVuQkJAg1bjSlp7IndNdjhGXOe/lQwE4jSI5nj96ZzS2H7+A8D7XA1Fb9YB3RmjGJ0TigXgDcksqUXGlRmoLR4KIiKgzOrWOUE/XXdcRMp29hKl/PdRu3Ycz74Lx5usjNy1NrA4JDgQAVP340ylKx2RrALy/GBER+SV3jt8MQm3orkHIsQih2VLT4jyh1hYhtNkFcksqYbZU48A3l/DP/H+3+F4AWPvYCI7QEBGRX3Ln+M3L53sAR4BpHEg6cvpKpVTAUl2HV7JOw2xt+fSa+M8+VmwrwgPxBmlEiYiIqDtiEOrmWlsr6MW02zA/5RasP1DitAJ0hE6DqUkxqG2ww3T2ktMojuOS+/aGCJuuLURERNRdMQh1Y60FlzJLDZ754JjTa/qgQNwzKAxHz13GG1+ekV53zOt5IN7g8orRDlzNmYiIujsGoW7KZhd46VPXg4uluh7bjze/S7tjgcX5Kbe4tWI0wNWciYio+/PogorUdd7adabVeTzucASp9QdLXH4PV3MmIqKegiNCfqilyc+NJzZnFZY5nd7qLAHnS+RdwdWciYioJ2AQ8jNt3Sj1gXgDDn17CUv+74RXPlsfFAhLdfuBaM0v7+BaQURE1CMwCPmRHccvNJvkDFyfxzP7vXyEBAe6PXLjjgdui2hx7aCmzlRc81obiIiIuhLnCPmJHcfLMPfD5iEI+GkejzdDUKRei1EDXbsUfv3BEtjsXIeTiIi6PwYhP5BVWIZnPsiHL7PF8gnxMOiDXKqt+rEeuSWVrW632QVMZy9ha8H3MJ29xNBERER+i6fGfMxmF1ixrcinbfjvETdifEIkbHaBkKBApwUYW9PaGkJtzXHivCIiIvI3HBHysdySSrfX7/E0S3U9Xtt5CofOXsJoF0+PtbSGkGOBx6b9caxVlFXYfB0jIiIiX+KIkI/5w+rM2cUVyC6uwFu7z7Zb67hha9M1hBwjWy2dBGt6fzJedk9ERP6CI0I+1t1WZxZoeQ2h9ka2Gt+fjIiIyF9wRMjHkuJCEanXwmypces+X76yIGVQi3N9XB3Z8ocRsJa0t4hlR97bmX0SEVHXYBDyMZVSgeUT4jHnvXxfN8UlsWG9Wnzd1ZGtsF4amM5e8lo46Ej46MwE75beG9orEHdEh+DYeQsqr9W5vU8iIuo6CiFEdxiI8Amr1Qq9Xg+LxQKdTufVz9px/ALmbSpAvc2/vx0fzrwLxpubT6iua7Bj8Iuft7kEgAKAPigAVdUN0mueDAcdCTSOCd5Nm+2ITmsfG+H2e1vjyj4b44gSEVHHuHP85hwhP5BVWIbfby/26xDU3o1W87673O46SAJwCkGA564o68gVa+1N8AauT/BuaR2ktt7bmvb22VhWYRlGv7ILU/96CPM2FmDqXw9h9Cu7eOUdEZGHMQj5mOMA7ok7yXtbS5OkHYsnbv/q+w7t051w0PQzHQs21jXYOxRoOjPBu6PLHjj2mXmg9dW5uQwBEVHX4RwhH+rIqIKvzE+5xel0js0u8NauM1h/4JxLCzC2pXHgaOm0W2OtzcmpvNZ6G1rbf2cmeHd20vcfPivG3/aXNDttV9dgx/OfFHIZAuoQnk6lzpDrzw+DkA/5w2KKrqq32bG14HuE99Hi8rU6PL/lhMfvffb5f0Y6WvvH19qcnLZCUGMXqqqdJmqH9da49L6WJoJ7YtmDsv/cTPftX47Az26PRFZhGZZ+fByXf2xo9T3uhEaSF67qTp0h558fTpZug7cnS6/4tBDrD37n8f12d/qgQDxwWwRGDewHgz5Impc0+pVdHg2OBp0GNQ12WH6sb3VULrRXIA4tTYE6wPksss0uMPqVXR5Z9kCpAGaMjsVf/3XO5fe8+ehwPDT8xk5+cse091ejXP+q9KXOTPon6ok/P+4cvxmE2uDNILTjeBnSP3D9iiM5M+g0mJo0AG98+bXXPkMBtPq9aPpXkeNA/8XJMp8F2dau3vO2tv5qfCDe0OLp0tBegXh4+I1IiTfIOhR5KyA6QnlrfyQ4VoPfv/j+HvO1Z9j2nJ7688Mg5CHeCkJZhWWY3U3WDZKDYLUKmgAlLrdyqs/xT39+yi2o+rEW/8z/N67U2LqugU2EBAci74UH3F600ZXatmra+qtR4PrX8ce6tr8uchlqb8qbpx1MZy9h6l8PtVvnq/DsaS19LQ06LaYmxSA2LFj6uQXAsOQCX/78eDPQunP85hyhLuYPd5snZz/W2fBjna3VUSHHax0ZkeqjVXk8NFX9WI/sIjMAuHxwdeXgcflaLX6/vQhma61Uow8KwJOj4jDn/xvY7pV57YUg4Kcr37rjUHtHtRYgPfW16O6rurfHZhc4dPYSTN9exNkfruHzQnOzGrO1xunfZ0hwIAA4zWNs/G+DI0o/8dXPjz/NSeKIUBu8MSLkavqm7u2JUbEYF29A4oC+uCsjx2mFaU/QBwXAUt36pOoZo2KlU1HZRWa3Fn5sSS+1Etfq7J3Yw0+661B7R3TFaYeePCKUVViGJR975sIMx1d31r1x+PSrMr84APsDX/z8dMWcJI4I+bHu+lcZuWf9gXNQArDbBe4c0Bc7i8o9uv+2QhAA/P3AOfz9wDlpQnhn/9rxVAgCWr7yraf+he7OWlUdPci0d79CR9hqbTFUf+XpKQSOr807+0qabZPjSKVDV//8tLeQrS+WCGEQ6mJnK676ugnURRxhxJcan+byN44/Crw5RO7rgNUVpx0a36+w6eldR09bWgzVn9nsAi99erLLPk/Oa3R19c9PV/xx4C6uLN2FbHaBd/d96+tmEPmF8D5ar66i7Q+3KXF1vanOrks1PiESax8bAYPeeT8GvbZbjnLkllR2eYhvayX5nq4rf378cU4bR4S60KGzl1DT4LlTDETdlVIBDI8Owf2v7/HKELm3Jyi7qitPO4xPiMQD8YYecYrRl1MI5Dp9oat+frrqjwN3yGJEaM2aNYiNjYVWq0VycjJyc3N90o75m4755HOJPEkT0PlfG3YBfHD4uw7f660tnbmZrqc5TjsAP51mcPDGaQeVUgHjzf3w0PAbYby5X7cMQUDXHgT96bN9rSt+fhx/HLS25/Zu8O0NPT4Ibdq0CQsXLsTy5cuRn5+PYcOGITU1FRUVFV3ajqs1DfjhqmevHCLyhYUP3OKR/XxX+aNLde7+hd6Zm+l6Q087bdUVkuJCYdC5dgscV7V3SPfFAViOuvqPA1f0+CD05z//GTNnzsQTTzyB+Ph4rFu3DsHBwfjHP/7Rpe145r0jXfp5RN7QNzgQT4yKQ6S+8381DwgNdqnO3b/Q/XEOwviESOxffD8+nHkX3nx0OD6ceRf2L76fIagVKqUCL/18SLt1T46KxYKUQc1CU0hwoLSWkINBr8XT98ZBAf85AMuVv/1x0KPnCNXV1SEvLw9Lly6VXlMqlUhJSYHJZOrSthw4K78JeNS99A0ORG2Dvc2FETMmDYU6QInlE+I7fGmzY17M48ZY/G1/icfnz/jjHATgp9MO5JrxCZFY99iIFtcR6hsciIxJQ6UD5tz7BzWb2wK0vLL0HTF9my8uKuN1hHzFn+a09eggdPHiRdhsNkRERDi9HhERgVOnTjWrr62tRW3tT1cqWK1Wj7WlC6YjkAy89ehwvPDpSZcXmOutCcDV2rbXHAKAF9Nuw69HxV3/jF3f4B8HSmCpbnlVXqDtg1RbGv/l7QhUnr5st6euqyNHjoOlY2Vp4HqYvOsm5/krrYXMll7zpwOw3PnLHwc9Ogi5KyMjAytWrPDKviN1alywco4QdUzjIBIQoGxzNGb+2EGIu6EXwvtokTigL5L+98s2w0qkXotfj4qTDgTzUgZh7v0D2z1QOA4ob+36BusPlDjdaLU1Tf/ydgyRe/Iv9J64ro6cqZQKjBoUhlGDwjy6T384AJN/6NG32Kirq0NwcDD++c9/YuLEidLr06dPR1VVFbZu3epU39KIUHR0tEdusfGDtRZ3/u+XndoH+be27mDfGQtSBmHu/YOcDtzuLELY1gq9CnhmOfvGCxeG9dYAArh4rRZhvTSAArh4tbbNv7y9sfChP93LiIi6Fu8+30hycjKSkpKwevVqAIDdbkdMTAzmzp2LJUuWtPleT99r7PaXdsJa0/5pCuo8tUqJm28IRsmlH1FT3/LaTX2DAzDpjv7QBQXig8OlKL/yUwgO7RWIh4ffCF2QGh/mlsJsbT6xNihQiaE36nFnXCjuvikMd8aFIu+7yzBba1B5tRahvdTX56EogAprDSqv1UEfFIhPv7qAIyWVqG5nTamm8yCacic8yDUU+HplaSLyDQahRjZt2oTp06fjnXfeQVJSEv7yl7/go48+wqlTp5rNHWrKGzdd9UYYClAqICBg68RajWG9AnG1tgE1DT/9OKhVCgw29MGUpGi8s+csSi83DwNqlRKDDb3RRxuIYf1DoA5QYuOR807BIUCpwG2G3ljwwGBYaupx8UotLl6twcnvrdCqVQjvo0GfoACUW2phtwtUWH/E+apa2OwCN4X1wsxRNyEgUIlDJZdw4XI1okKCcFdcPyhVCly8Wouw3hrYbQKHz11C0zkETiMVbYxOtHXAdGxrHHAM+qBOHVSbfl7igL44UlLZ5jyIzmIoICK5YBBq4q233sKrr74Ks9mM4cOHY9WqVUhOTm73fd4IQsD102QPvbUPZVfqoFQAt0X0QkxoML65WI0+mgDcEtEHIcGBUCmVMN7cD3fGhuLIuUqYzl4CIJAc+1MIaO0KicQBfaXRiYtXanH5x1ooFUrcGdsXRWUW5BT/AEBgXLwBvx4VB3WAst0DZV2DHRsOnsORc5cQrA7A5BH9cffAsGYHUx5wiYjIlxiEPMRbQYiIiIi8x53jd49fUJGIiIioNQxCREREJFsMQkRERCRbDEJEREQkWwxCREREJFsMQkRERCRbDEJEREQkWwxCREREJFsMQkRERCRbAb5ugD9zLLpttVp93BIiIiJyleO47crNMxiE2nDlyhUAQHR0tI9bQkRERO66cuUK9Hp9mzW811gb7HY7Lly4gD59+kCh8OxNQ61WK6Kjo3H+/HnZ3ceMfWff2Xf5YN/Zd1/0XQiBK1euICoqCkpl27OAOCLUBqVSif79+3v1M3Q6nez+gTiw7+y73LDv7Lvc+LLv7Y0EOXCyNBEREckWgxARERHJFoOQj2g0GixfvhwajcbXTely7Dv7LjfsO/suN92p75wsTURERLLFESEiIiKSLQYhIiIiki0GISIiIpItBiEiIiKSLQYhD1mzZg1iY2Oh1WqRnJyM3NzcNus3b96MwYMHQ6vVYujQodixY4fTdiEEli1bhsjISAQFBSElJQVnzpzxZhc6zJN9r6+vx+LFizF06FD06tULUVFR+NWvfoULFy54uxsd4unve2OzZ8+GQqHAX/7yFw+32jO80ffi4mL8/Oc/h16vR69evXDnnXeitLTUW13oME/3/erVq5g7dy769++PoKAgxMfHY926dd7sQoe50/eTJ09i8uTJiI2NbfNn2d2vp694uu8ZGRm488470adPH4SHh2PixIk4ffq0F3vQcd74vju8/PLLUCgUmD9/vmcb7SpBnbZx40ahVqvFP/7xD3Hy5Ekxc+ZMERISIsrLy1usP3DggFCpVGLlypWiqKhIvPDCCyIwMFCcOHFCqnn55ZeFXq8XW7ZsEV999ZX4+c9/LuLi4kR1dXVXdcslnu57VVWVSElJEZs2bRKnTp0SJpNJJCUlicTExK7slku88X13+Pjjj8WwYcNEVFSUeOONN7zcE/d5o+/ffPONCA0NFYsWLRL5+fnim2++EVu3bm11n77ijb7PnDlT3HzzzWL37t2ipKREvPPOO0KlUomtW7d2Vbdc4m7fc3NzxW9/+1vx4YcfCoPB0OLPsrv79BVv9D01NVWsX79eFBYWioKCAvGzn/1MxMTEiKtXr3q5N+7xRt8b18bGxorbb79dzJs3zzsdaAeDkAckJSWJ9PR06bnNZhNRUVEiIyOjxfpHHnlEpKWlOb2WnJwsnn76aSGEEHa7XRgMBvHqq69K26uqqoRGoxEffvihF3rQcZ7ue0tyc3MFAPHdd995ptEe4q2+//vf/xY33nijKCwsFAMGDPDLIOSNvk+ZMkU89thj3mmwB3mj70OGDBG///3vnWpGjBghfve733mw5Z3nbt8ba+1nuTP77Ere6HtTFRUVAoDYu3dvZ5rqcd7q+5UrV8SgQYNEdna2GDNmjM+CEE+NdVJdXR3y8vKQkpIivaZUKpGSkgKTydTie0wmk1M9AKSmpkr1JSUlMJvNTjV6vR7Jycmt7tMXvNH3llgsFigUCoSEhHik3Z7grb7b7XY8/vjjWLRoEYYMGeKdxneSN/put9vx2Wef4ZZbbkFqairCw8ORnJyMLVu2eK0fHeGt7/vdd9+NTz/9FN9//z2EENi9eze+/vprjBs3zjsd6YCO9N0X+/SGrmqnxWIBAISGhnpsn53lzb6np6cjLS2t2b+PrsYg1EkXL16EzWZDRESE0+sREREwm80tvsdsNrdZ7/ivO/v0BW/0vamamhosXrwYU6dO9aubFnqr76+88goCAgLwP//zP55vtId4o+8VFRW4evUqXn75ZYwfPx5ffPEFHn74YUyaNAl79+71Tkc6wFvf99WrVyM+Ph79+/eHWq3G+PHjsWbNGtx7772e70QHdaTvvtinN3RFO+12O+bPn49Ro0YhISHBI/v0BG/1fePGjcjPz0dGRkZnm9hpvPs8+a36+no88sgjEEJg7dq1vm6O1+Xl5eHNN99Efn4+FAqFr5vTpex2OwDgoYcewoIFCwAAw4cPx8GDB7Fu3TqMGTPGl83zutWrV+PQoUP49NNPMWDAAOzbtw/p6emIiory+V/L1DXS09NRWFiI/fv3+7opXnf+/HnMmzcP2dnZ0Gq1vm4OR4Q6KywsDCqVCuXl5U6vl5eXw2AwtPgeg8HQZr3jv+7s0xe80XcHRwj67rvvkJ2d7VejQYB3+v6vf/0LFRUViImJQUBAAAICAvDdd9/h2WefRWxsrFf60RHe6HtYWBgCAgIQHx/vVHPbbbf51VVj3uh7dXU1nn/+efz5z3/GhAkTcPvtt2Pu3LmYMmUKXnvtNe90pAM60ndf7NMbvN3OuXPnYvv27di9ezf69+/f6f15kjf6npeXh4qKCowYMUL6Xbd3716sWrUKAQEBsNlsnmi6yxiEOkmtViMxMRE5OTnSa3a7HTk5OTAajS2+x2g0OtUDQHZ2tlQfFxcHg8HgVGO1WnH48OFW9+kL3ug78FMIOnPmDL788kv069fPOx3oBG/0/fHHH8fx48dRUFAgPaKiorBo0SLs3LnTe51xkzf6rlarceeddza7dPjrr7/GgAEDPNyDjvNG3+vr61FfXw+l0vnXsUqlkkbK/EFH+u6LfXqDt9ophMDcuXPxySefYNeuXYiLi/NEcz3KG30fO3YsTpw44fS7buTIkZg2bRoKCgqgUqk81XzX+GSKdg+zceNGodFoRGZmpigqKhKzZs0SISEhwmw2CyGEePzxx8WSJUuk+gMHDoiAgADx2muvieLiYrF8+fIWL58PCQkRW7duFcePHxcPPfSQ314+78m+19XViZ///Oeif//+oqCgQJSVlUmP2tpan/SxNd74vjflr1eNeaPvH3/8sQgMDBTvvvuuOHPmjFi9erVQqVTiX//6V5f3ry3e6PuYMWPEkCFDxO7du8W3334r1q9fL7RarXj77be7vH9tcbfvtbW14tixY+LYsWMiMjJS/Pa3vxXHjh0TZ86ccXmf/sIbfZ8zZ47Q6/Viz549Tr/rfvzxxy7vX1u80femfHnVGIOQh6xevVrExMQItVotkpKSxKFDh6RtY8aMEdOnT3eq/+ijj8Qtt9wi1Gq1GDJkiPjss8+cttvtdvHiiy+KiIgIodFoxNixY8Xp06e7oitu82TfS0pKBIAWH7t37+6iHrnO09/3pvw1CAnhnb7//e9/FwMHDhRarVYMGzZMbNmyxdvd6BBP972srEz8+te/FlFRUUKr1Ypbb71VvP7668Jut3dFd9ziTt9b+/c8ZswYl/fpTzzd99Z+161fv77rOuUib3zfG/NlEFIIIUQXDT4RERER+RXOESIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItliECIiIiLZYhAiIiIi2WIQIiIiItn6fyiKjhr2ZPKpAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 0 COIs with pop >  1.005 * 786629.867\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "after splitting the 0 big COIs, we have a total of 642 units\n",
      "building vtd-based COIgeom for unit 0\n",
      "building vtd-based COIgeom for unit 100\n",
      "building vtd-based COIgeom for unit 200\n",
      "building vtd-based COIgeom for unit 300\n",
      "building vtd-based COIgeom for unit 400\n",
      "building vtd-based COIgeom for unit 500\n",
      "building vtd-based COIgeom for unit 600\n"
     ]
    }
   ],
   "source": [
    "COItractList = [muniTractList[c].copy() for c in range(nCOIs)]  #nomenclature\n",
    "print(\"compute and plot the COI pops by area, ID the big COIs\")\n",
    "COIpop =  [np.sum([tractPop[t] for t in muniTractList[u] ]) for u in range(nCOIs)]\n",
    "COIarea = [np.sum([tractArea[t] for t in muniTractList[u] ]) for u in range(nCOIs)]\n",
    "plt.scatter(COIarea,COIpop)\n",
    "plt.axhline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "isBigCOI, bigCOIlist = [0]*nCOIs,list()\n",
    "for u in range(nCOIs):\n",
    "    if COIpop[u] > maxDistrictPop :  #1.05 * aDP:  #1.005 for Congrl, 1.05 for legisl\n",
    "        isBigCOI[u] = 1\n",
    "        bigCOIlist.append(u)\n",
    "        print(\"found a COI with pop/aDP =\",r3(COIpop[u]/aDP) )\n",
    "nBigCOIs = len(bigCOIlist)\n",
    "print(\"there are\",nBigCOIs,\"COIs with pop > \",r3(maxDistrictPop/aDP),\"*\",r3(aDP) )\n",
    "\n",
    "COIgeom = [tractGeom[muniTractList[c][0]] for c in range(nCOIs) ]  # we will only build the big ones, but have to start them\n",
    "for c in bigCOIlist:    \n",
    "    for v in muniTractList[c]:\n",
    "        COIgeom[c] = COIgeom[c].union(tractGeom[v])\n",
    "    plotPoly(COIgeom[c])\n",
    "    plotCenter(r3(COIpop[c]/aDP), COIgeom[c])\n",
    "plotPoly(MAP)\n",
    "plt.show()\n",
    "\n",
    "for u in bigCOIlist:  #now spli them up\n",
    "    for i,v in enumerate(muniTractList[u].copy()) :\n",
    "        if i == 0:\n",
    "            muniTractList[u] = [v]\n",
    "        else:\n",
    "            muniTractList.append([v])\n",
    "nUnits = len(muniTractList)\n",
    "print(\"after splitting the\",nBigCOIs,\"big COIs, we have a total of\",nUnits,\"units\")\n",
    "\n",
    "unitGeom = [tractGeom[muniTractList[u][0]] for u in range(nUnits)]\n",
    "unitPop  = [ np.sum([tractPop[v] for v in muniTractList[u] ] ) for u in range(nUnits) ]\n",
    "for u in range(nUnits):\n",
    "    if u%100 == 0:\n",
    "        print(\"building vtd-based COIgeom for unit\",u)\n",
    "    for v in muniTractList[u]:\n",
    "        unitGeom[u] = unitGeom[u].union(tractGeom[v])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "89650709-3535-48dd-807f-47bbf8da391b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, where are the fairly big units, with pop > 0.3 aDP?\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"OK, where are the fairly big units, with pop > 0.3 aDP?\")\n",
    "plotPoly(MAP)\n",
    "for u in range(nUnits):\n",
    "    if unitPop[u] > 0.3 * aDP:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(r3(unitPop[u]/aDP),unitGeom[u])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "7254555b-bf6a-4ccb-8d5d-628d56773723",
   "metadata": {},
   "outputs": [],
   "source": [
    "unitTractList = [muniTractList[u].copy() for u in range(nUnits)] #nomenclature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "aaa8dc85-9a68-4e49-992d-42c8c5b52254",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Create the unit topology. First assign each vtd's unit number (reverse of unitTractList)\n",
      "build unit nbr list based on adjacent vtd's\n"
     ]
    }
   ],
   "source": [
    "print(\"Create the unit topology. First assign each vtd's unit number (reverse of unitTractList)\")\n",
    "tractUnitNo = [-999 for v in range(nTracts)]\n",
    "for u in range(nUnits):\n",
    "    for v in unitTractList[u]:\n",
    "        tractUnitNo[v] = u\n",
    "unitNbrSet = [set() for u in range(nUnits)]\n",
    "print(\"build unit nbr list based on adjacent vtd's\")  #these are list-based, not geom-based\n",
    "for v in range(nTracts):\n",
    "    u = tractUnitNo[v]\n",
    "    for vv in vtdNbrs[v]:\n",
    "        uu = tractUnitNo[vv]\n",
    "        if uu != u  and uu >= 0:  #avoid assigning vtd's relegated to unit -999\n",
    "            unitNbrSet[u].add(uu)\n",
    "unitNbrs = [list(unitNbrSet[u]) for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5cb669f9-5e91-4fd2-8195-e0791a90466a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for u in range(nUnits):\n",
    "    if len(unitNbrs[u]) == 0:\n",
    "        print(u, \"is a neighborless unit, here I plot its county and its county's other units\")\n",
    "        for v in unitTractList[u]:\n",
    "            plotPoly(tractGeom[v])\n",
    "            plotPoly(countyGeom[countyNo[v]])\n",
    "            c = countyNo[v]\n",
    "        for uu in range(nUnits):\n",
    "            if countyNo[unitTractList[uu][0]] == c:\n",
    "                for v in unitTractList[uu]:\n",
    "                    plotCenter(uu,tractGeom[v])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "437c1607-5cb6-49b0-855d-45bdda404f1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now let's find singles and small clusters surrounded by big units\n",
      "This is a more generalized surround operation\n",
      "checking for big surround for unit 0\n",
      "checking for big surround for unit 500\n",
      "I found a total of 21 surrounded units. nonduplicated nSurrounders = 13\n"
     ]
    }
   ],
   "source": [
    "print(\"Now let's find singles and small clusters surrounded by big units\")\n",
    "print(\"This is a more generalized surround operation\")\n",
    "surrounded, surrounders = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if u%500 == 0:\n",
    "        print(\"checking for big surround for unit\",u)\n",
    "    nbrNbrs = [len(unitNbrs[uu]) for uu in unitNbrs[u]]\n",
    "    bigNbrU = unitNbrs[u][nbrNbrs.index(np.max(nbrNbrs))]  #of the surrounded neighbors, the surrounder should have the most neighbors\n",
    "    loopNo, foundSet = 0, set(unitNbrs[u]).difference({bigNbrU})\n",
    "    while len(foundSet) < 20 and loopNo < 7:\n",
    "        loopNo +=1\n",
    "        for uu in foundSet:\n",
    "            foundSet = foundSet.union(set(unitNbrs[uu]).difference({bigNbrU}) )\n",
    "    if len(foundSet) < 20:\n",
    "        surrounded.append(u)\n",
    "        surrounders.append(bigNbrU)\n",
    "print(\"I found a total of\",len(surrounded),\"surrounded units. nonduplicated nSurrounders =\",len(set(surrounders)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "5650516e-01b0-4e86-b69b-4e77ff5c8317",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[] are both surrounders and surrounded\n"
     ]
    }
   ],
   "source": [
    "sandwichSurrounds = list()\n",
    "for u in surrounded:\n",
    "    if u in surrounders:\n",
    "        sandwichSurrounds.append(u)\n",
    "print(sandwichSurrounds,\"are both surrounders and surrounded\")\n",
    "for u in sandwichSurrounds:\n",
    "    plotPoly(unitGeom[u])\n",
    "    plotCenter(u,unitGeom[u])\n",
    "    for uu in unitNbrs[u]:\n",
    "        plotPoly(unitGeom[uu],0.2)\n",
    "        plotCenter(uu,unitGeom[uu],8)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "aad9cf17-f19a-4e88-b497-3197793074bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now capture surrounds.  If any are both surrounders and surrounded, we handle them in second loop\n",
      "now update unitPops and CPs\n",
      "statePop, updated sum unitPops now 11799448 11799448.0\n",
      "total assigned VTDs = 8934 + 7 vs 8941\n"
     ]
    }
   ],
   "source": [
    "print(\"now capture surrounds.  If any are both surrounders and surrounded, we handle them in second loop\")\n",
    "for i, u in enumerate(surrounded):\n",
    "    uu = surrounders[i]\n",
    "    if u in sandwichSurrounds:\n",
    "        print(u,\"is both a surrounder and surrounded.  Handle this one later\")\n",
    "    else:\n",
    "        unitNbrs[uu].remove(u)\n",
    "        unitNbrs[u] = list()\n",
    "        unitTractList[uu] += unitTractList[u]\n",
    "        for t in unitTractList[u]:\n",
    "            unitGeom[uu] = unitGeom[uu].union(tractGeom[t])\n",
    "            tractUnitNo[t] = uu\n",
    "        unitTractList[u] = list()\n",
    "for u in sandwichSurrounds:\n",
    "    print(\"now working on sandwichSurround\",u)\n",
    "    uu = surrounders[surrounded.index(u)]\n",
    "    unitNbrs[uu].remove(u)\n",
    "    unitNbrs[u] = list()\n",
    "    unitTractList[uu] += unitTractList[u]\n",
    "    for t in unitTractList[u]:\n",
    "        unitGeom[uu] = unitGeom[uu].union(tractGeom[t])\n",
    "        tractUnitNo[t] = uu\n",
    "    unitTractList[u] = list()\n",
    "print(\"now update unitPops and CPs\")\n",
    "unitPop = [np.sum([tractPop[b] for b in unitTractList[u] ]) for u in range(nUnits) ]\n",
    "unitCP = [unitGeom[u].centroid for u in range(nUnits) ]\n",
    "print(\"statePop, updated sum unitPops now\",np.sum(tractPop),np.sum(unitPop))\n",
    "print(\"total assigned VTDs =\",np.sum([len(unitTractList[u]) for u in range(nUnits)]),\"+\",len(skipList),\"vs\",nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "998a5951-61a4-414f-9443-4986a9176c10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, cut out the surrounded units and transfer their pops.  We already fused the geoms and bg lists\n",
      "To be safe, we will reassign unit nbrs from vtd topology \n",
      "state pop = 11799448 = 11799448\n",
      "reassign unit nbrs based on vtd topology\n",
      "build unit nbr list based on adjacent vtd's\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, cut out the surrounded units and transfer their pops.  We already fused the geoms and bg lists\")\n",
    "print(\"To be safe, we will reassign unit nbrs from vtd topology \")\n",
    "oldPop, oldGeom, oldList = unitPop.copy(), unitGeom.copy(), [unitTractList[u].copy() for u in range(nUnits)]\n",
    "oldNbrs = [unitNbrs[u].copy() for u in range(nUnits) ]\n",
    "\n",
    "unitPop, unitGeom, unitTractList, oldUnitNo, nOldUnits = list(), list(), list(), list(), nUnits\n",
    "for u in range(nOldUnits):\n",
    "    if u not in surrounded:\n",
    "        oldUnitNo.append(u)\n",
    "        unitPop.append(np.sum([tractPop[t] for t in oldList[u] ]) )\n",
    "        unitGeom.append(oldGeom[u])\n",
    "        unitTractList.append(oldList[u].copy())\n",
    "nUnits = len(unitPop)\n",
    "    \n",
    "tractUnitNo = [-999 for t in range(nTracts)]  #need to reset after surrounds\n",
    "for u in range(nUnits):\n",
    "    for v in unitTractList[u]:\n",
    "        tractUnitNo[v] = u\n",
    "print(\"state pop =\",np.sum(tractPop),\"=\",np.sum([np.sum([tractPop[b] for b in unitTractList[u] ]) for u in range(nUnits)]) )\n",
    "\n",
    "print(\"reassign unit nbrs based on vtd topology\")\n",
    "unitNbrSet = [set() for u in range(nUnits)]\n",
    "print(\"build unit nbr list based on adjacent vtd's\")  #these are list-based, not geom-based\n",
    "for v in range(nTracts):\n",
    "    u = tractUnitNo[v]\n",
    "    for vv in vtdNbrs[v]:\n",
    "        uu = tractUnitNo[vv]\n",
    "        if uu != u  and uu >= 0:  #avoid assigning vtd's relegated to unit -999\n",
    "            unitNbrSet[u].add(uu)\n",
    "unitNbrs = [list(unitNbrSet[u]) for u in range(nUnits)]\n",
    "unitCP = [unitGeom[u].centroid for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8e1d20ee-9b51-462d-88ec-7ce534024df7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "did we create any bigUs via surround?\n"
     ]
    }
   ],
   "source": [
    "print(\"did we create any bigUs via surround?\")\n",
    "for u in range(nUnits):\n",
    "    if unitPop[u] > 1.05 * aDP:\n",
    "        print(u,r3(unitPop[u]/aDP))\n",
    "        plotPoly(unitGeom[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "438694fa-7eb2-4628-8a32-08de211f79c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "check for discontig units\n",
      "we found a total of 0 Units that were not rook-contiguous\n"
     ]
    }
   ],
   "source": [
    "print(\"check for discontig units\")\n",
    "isContigUnit = [False]*nUnits\n",
    "nonContigUnits = list()\n",
    "for c in range(nUnits):\n",
    "    isContigUnit[c] = isContiguous(unitTractList[c],vtdNbrs)[0]\n",
    "    if not isContigUnit[c]:\n",
    "        nonContigUnits.append(c)\n",
    "print(\"we found a total of\",len(nonContigUnits),\"Units that were not rook-contiguous\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2f7b113b-fe6a-480b-bc2d-9ccf8a3d7a0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "any remaining one- or zero-neighbor units?\n"
     ]
    }
   ],
   "source": [
    "print(\"any remaining one- or zero-neighbor units? Plot them, their neighbor and county\")\n",
    "for u in range(nUnits):\n",
    "    if len(unitNbrs[u]) < 2:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "        for uu in unitNbrs[u] :\n",
    "            plotPoly(unitGeom[uu],0.2)\n",
    "        plotPoly(countyGeom[countyNo[unitTractList[u][0]]] )\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "ff0c9aa3-1931-4e8c-8689-05018af239f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining border counties\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we determine and plot the border vtds\n",
      "IDing border vtds in county 0\n",
      "IDing border vtds in county 19\n",
      "IDing border vtds in county 49\n",
      "IDing border vtds in county 80\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining border counties\")\n",
    "\n",
    "Kelley, Knbrs = 3739, [3622, 3627] #FILL THIS IN BASED ON ABOVE 5879, [5870, 5871] for bgs; 3739, [3622, 3627] for vtds\n",
    "#Kelley, Knbrs = 5879, [5870, 5871]  #in case we used previously saved bg topology vs. defining in this run\n",
    "borderCounties = list()\n",
    "for c in range(nCounties):\n",
    "    if countyGeom[c].intersects(mainMAP.exterior):\n",
    "        borderCounties.append(c)\n",
    "        plotPoly(countyGeom[c])\n",
    "plt.show()\n",
    "usePrevious = False\n",
    "if usePrevious:\n",
    "    print(\"We will use previously defined and read-in border vtds\")\n",
    "else:\n",
    "    print(\"Now we determine and plot the border vtds\")\n",
    "    borderVTDs = list()\n",
    "    for i,c in enumerate(borderCounties):\n",
    "        if i%10 == 0:\n",
    "            print(\"IDing border vtds in county\",c)\n",
    "        for v in countyTractList[c]:\n",
    "            if isRookAdj(tractGeom[v],mainMAP.exterior) or v == Kelley:\n",
    "                borderVTDs.append(v)\n",
    "                plotPoly(tractGeom[v])\n",
    "    plt.show()\n",
    "    isBorderVTD = [0]*nTracts\n",
    "    for v in borderVTDs:\n",
    "        isBorderVTD[v] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "febb7f03-b536-4d17-8d61-ea4e9599af7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Make a tentative set / list of border units. Numbered units have zero nonborder neighbors; will be surrounded next block\n",
      "'pop' shows pop/aDP for pop/aDP > 0.5 in a border unit\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Make a tentative set / list of border units. Numbered units have zero nonborder neighbors; will be surrounded next block\")\n",
    "print(\"'pop' shows pop/aDP for pop/aDP > 0.5 in a border unit\")\n",
    "isBorder = [0]*nUnits\n",
    "borderSet = set()\n",
    "for v in borderVTDs:\n",
    "    u = tractUnitNo[v]\n",
    "    borderSet.add(u)\n",
    "    isBorder[u] = 1\n",
    "borderUnits = list(borderSet)\n",
    "surroundedBorderUnits = list()\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "    if len(set(unitNbrs[u]).difference(set(borderUnits)) ) == 0:\n",
    "        plotCenter(u,unitGeom[u],9)\n",
    "        surroundedBorderUnits.append(u)\n",
    "    if unitPop[u] > 0.5 * aDP:\n",
    "        plotCenter(\"pop\"+str(r3(unitPop[u]/aDP)),unitGeom[u],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "1be34afe-7873-48d9-9b13-e8b48caee20b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a couple of these are big corners, not truly surrounded. Add a cattycorner neighbor\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cornerUnitSet = set() \n",
    "print(\"a couple of these are big corners, not truly surrounded. Add a cattycorner neighbor\")\n",
    "for u in surroundedBorderUnits:\n",
    "    for uu in range(nUnits):\n",
    "        if uu not in unitNbrs[u] and uu != u and unitGeom[u].intersects(unitGeom[uu]):\n",
    "            unitNbrs[u].append(uu)  #this works as if a tiny connection existed across the four-square\n",
    "            unitNbrs[uu].append(u)\n",
    "            plotPoly(unitGeom[uu])\n",
    "            plotCenter(\"corner\"+str(uu),unitGeom[uu])\n",
    "            cornerUnitSet.add(u)  \n",
    "            plotPoly(unitGeom[u],2)\n",
    "            plotCenter(u,unitGeom[u])\n",
    "            for uuu in unitNbrs[u]:\n",
    "                plotPoly(unitGeom[uuu], 0.3)\n",
    "            plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0f99211c-4b63-45af-b6df-af41b4627c7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will collapse unit 483 into unit 471\n",
      "we will collapse unit 73 into unit 209\n",
      "we will collapse unit 618 into unit 481\n",
      "we will collapse unit 76 into unit 209\n",
      "we will collapse unit 142 into unit 84\n",
      "we will collapse unit 143 into unit 85\n",
      "we will collapse unit 48 into unit 43\n",
      "we will collapse unit 367 into unit 349\n",
      "we will collapse unit 567 into unit 419\n",
      "we will collapse unit 440 into unit 438\n",
      "we will collapse unit 511 into unit 477\n"
     ]
    }
   ],
   "source": [
    "surroundedBorderUnits = list(set(surroundedBorderUnits).difference(cornerUnitSet))\n",
    "\n",
    "surrounderBorderUnits = list()\n",
    "for u in surroundedBorderUnits:\n",
    "    candidates = list()\n",
    "    for uu in set(unitNbrs[u]).difference(set(surroundedBorderUnits)):\n",
    "        candidates.append(uu)\n",
    "    uuDists = [unitCP[u].distance(unitCP[uu]) for uu in candidates]\n",
    "    uuu = candidates[uuDists.index(np.min(uuDists)) ]\n",
    "    print(\"we will collapse unit\",u,\"into unit\",uuu)\n",
    "    surrounderBorderUnits.append(uuu)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "4db9ba9b-cb0c-4a6e-8ebe-284f3e0b78c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for u in surrounderBorderUnits:\n",
    "    if u in surroundedBorderUnits:\n",
    "        print(\"uhoh, we named\",u,\"as a surrounder but it was surrounded\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "3cf86982-9153-4e34-abde-2016afcb2e31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "absorbing surrounded border units ...\n",
      "we now have 610 total units.\n"
     ]
    }
   ],
   "source": [
    "oldList = [unitTractList[u].copy() for u in range(nUnits)] #safekeeping\n",
    "newCornerUnitList = list()\n",
    "print(\"absorbing surrounded border units ...\")\n",
    "for u in range(nUnits):\n",
    "    if u in surroundedBorderUnits:\n",
    "        uu = surrounderBorderUnits[surroundedBorderUnits.index(u)]\n",
    "        oldList[uu] += oldList[u]\n",
    "        oldList[u] = list()\n",
    "        \n",
    "unitTractList, unitPop = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if u not in surroundedBorderUnits:\n",
    "        oldUnitNo.append(u)\n",
    "        unitTractList.append(oldList[u])\n",
    "        unitPop.append(np.sum([tractPop[b] for b in oldList[u] ]))\n",
    "        if u in cornerUnitSet:\n",
    "            newCornerUnitList.append(len(unitPop)-1)\n",
    "nUnits = len(unitPop)\n",
    "print(\"we now have\",nUnits,\"total units.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "3edc4190-01a7-424d-9a46-a643cdfac213",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating reverse bg assignments ...\n",
      "state pop = 11799448 = 11799448\n",
      "reassign unit nbrs based on v topology. We'll add back cattycorners in a later block\n",
      "build unit nbr list based on adjacent vtd's\n",
      "rebuilding geom for unit 0\n",
      "rebuilding geom for unit 500\n"
     ]
    }
   ],
   "source": [
    "print(\"updating reverse bg assignments ...\")\n",
    "tractUnitNo = [-999 for t in range(nTracts)]  #need to reset after surrounds\n",
    "for u in range(nUnits):\n",
    "    for v in unitTractList[u]:\n",
    "        tractUnitNo[v] = u\n",
    "print(\"state pop =\",np.sum(tractPop),\"=\",np.sum([np.sum([tractPop[v] for v in unitTractList[u] ]) for u in range(nUnits)]) \n",
    "\n",
    "print(\"reassign unit nbrs based on v topology. We'll add back cattycorners in a later block\")\n",
    "unitNbrSet = [set() for u in range(nUnits)]\n",
    "print(\"build unit nbr list based on adjacent vtd's\")  #these are list-based, not geom-based\n",
    "for v in range(nTracts):\n",
    "    u = tractUnitNo[v]\n",
    "    for vv in vtdNbrs[v]:\n",
    "        uu = tractUnitNo[vv]\n",
    "        if uu != u  and uu >= 0:  #avoid assigning bg's relegated to unit -999\n",
    "            unitNbrSet[u].add(uu)\n",
    "unitNbrs = [list(unitNbrSet[u]) for u in range(nUnits)]\n",
    "\n",
    "unitGeom = list()\n",
    "for u in range(nUnits):\n",
    "    if u%500 == 0:\n",
    "        print(\"rebuilding geom for unit\",u)\n",
    "    geo = tractGeom[unitTractList[u][0]]\n",
    "    for b in unitTractList[u]:\n",
    "        if b != unitTractList[u][0]:\n",
    "            geo = geo.union(tractGeom[b])\n",
    "    unitGeom.append(geo)\n",
    "\n",
    "unitCP = [unitGeom[u].centroid for u in range(nUnits)]\n",
    "unitPop = [np.sum([tractPop[v] for v in unitTractList[u] ]) for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "fe6787ab-5361-49ba-b670-347a16db92d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "need to add back in the cattycorners\n",
      "connecting corner 122 to its catty 126\n",
      "connecting corner 432 to its catty 477\n"
     ]
    }
   ],
   "source": [
    "print(\"need to add back in the cattycorners\")\n",
    "for u in newCornerUnitList:\n",
    "    for uu in range(nUnits):\n",
    "        if uu not in unitNbrs[u] and uu != u and unitGeom[u].intersects(unitGeom[uu]):\n",
    "            unitNbrs[u].append(uu)  #this works as if a tiny connection existed across the four-square\n",
    "            unitNbrs[uu].append(u)\n",
    "            print(\"connecting corner\",u,\"to its catty\",uu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "8be9d58a-2bce-4548-88ee-ddab694ece8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "unitPop = [np.sum([tractPop[v] for v in unitTractList[u] ]) for u in range(nUnits)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "076b2362-107e-438c-912b-9725870451d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "also need to update border units list. Numbered units have zero nonborder neighbors; should have already been surrounded\n",
      "'pop' shows pop/aDP for pop/aDP > 0.5 in a border unit\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"also need to update border units list. Numbered units have zero nonborder neighbors; should have already been surrounded\")\n",
    "print(\"'pop' shows pop/aDP for pop/aDP > 0.5 in a border unit\")\n",
    "isBorder = [0]*nUnits\n",
    "borderSet = set()\n",
    "for v in borderVTDs:\n",
    "    u = tractUnitNo[v]\n",
    "    borderSet.add(u)\n",
    "    isBorder[u] = 1\n",
    "borderUnits = list(borderSet)\n",
    "surroundedBorderUnits = list()\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u])\n",
    "    if len(set(unitNbrs[u]).difference(set(borderUnits)) ) == 0:\n",
    "        plotCenter(u,unitGeom[u],9)\n",
    "        surroundedBorderUnits.append(u)\n",
    "    if unitPop[u] > 0.35 * aDP:\n",
    "        plotCenter(\"pop\"+str(r3(unitPop[u]/aDP)),unitGeom[u],8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "37df254c-38b2-4eae-931e-044e0f8fc9c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "unitCountyNo = [countyNo[unitTractList[u][0]] for u in range(nUnits)]  #this may be off for county-straddling units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "e0545c19-3336-4bad-9acf-5eb90106ff6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the unsplittable units, above 0.35 of aDP or a corner border unit\n",
      "122 14456   starts in county 3\n",
      "432 9990   starts in county 52\n",
      "451 565995   starts in county 24\n",
      "471 365374   starts in county 30\n",
      "554 373479   starts in county 17\n"
     ]
    }
   ],
   "source": [
    "unsplittableUnits = list()\n",
    "minSplitRatio = 0.35\n",
    "print(\"Here are the unsplittable units, above\",minSplitRatio,\"of aDP or a corner border unit\")\n",
    "for u in range(nUnits):\n",
    "    if (unitPop[u] < maxDistrictPop and unitPop[u] > minSplitRatio * aDP ) or u in newCornerUnitList:\n",
    "        unsplittableUnits.append(u)\n",
    "        print(u, r3(unitPop[u]),\"  starts in county\",unitCountyNo[u])\n",
    "#print(\"and FYI, which ones are 0.4 - 0.5 aDP?\")  #this is FYI for 99seat maps only\n",
    "#for u in range(nUnits):\n",
    "#    if unitPop[u] < 0.5*avgDistrictPop and unitPop[u] > 0.4 * avgDistrictPop:\n",
    "#        print(\"   \",u, r3(unitPop[u]),unitCountyNo[u])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "5add9c83-4d1d-41e3-8393-e31aab496c41",
   "metadata": {},
   "outputs": [],
   "source": [
    "date = \"29Dec\"\n",
    "nUnits = len(unitPop)\n",
    "unitNo = [u for u in range(nUnits)]\n",
    "onBorder = [0 for u in range(nUnits)]\n",
    "unitCPx, unitCPy = [unitCP[u].x for u in range(nUnits)], [unitCP[u].y for u in range(nUnits)]\n",
    "unitCountyNo = [countyNo[unitTractList[u][0]] for u in range(nUnits)]  #this may be off for county-straddling units\n",
    "cantSplit = [0]*nUnits\n",
    "for u in range(nUnits):\n",
    "    if u in unsplittableUnits:\n",
    "        cantSplit[u] = 1\n",
    "for u in borderUnits:\n",
    "    onBorder[u] = 1\n",
    "nbrDF = pd.DataFrame( {\"unitNo\":unitNo,\"centroid x\":unitCPx,\"centroid y\":unitCPy,\"unitPop\":unitPop,\"onBorder\":onBorder,\n",
    "                       \"cantSplit\":cantSplit,\"unitNbrs\":unitNbrs, \"unitCountyNo\":unitCountyNo,\"unitTractList\":unitTractList} )\n",
    "outname = STATE+str(nDistricts)+\"schoolUnitTopologies_\"+date+\".csv\" \n",
    "outpath = \"state_map_files/\"+outname\n",
    "nbrDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a9ef2cd9-ff7c-47c8-bb88-814eb347702b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "610\n"
     ]
    }
   ],
   "source": [
    "print(nUnits)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "fe6b3dde-10af-4e01-ab58-49df834d85bf",
   "metadata": {},
   "source": [
    "Big Ohio cities list, and their 2020 Census pops:\n",
    "Akron - 190,469.   Enforce 0% or 60%+.  My calcn nearly on target\n",
    "Cincy - 309,317. Enforce 0% or 80%+   My calcn nearly on-target.\n",
    "Cleveland - 372,624 = 3 whole districts.  I count 417,000 which likely includes crannies. --> sprawling, enforce > 0.2\n",
    "Columbus - ~800,000 in Franklin County - seven whole districts but sprawling.  10 or 11 reasonable, no restrictions\n",
    "Dayton - 137,644.  Enforce 0% or 20%+  My calcn nearly on target\n",
    "Toledo - 270,871. No restriction.   My calcn nearly on target\n",
    "--------\n",
    "Parma 81,146 -- I AM OVERCOUNTING, but ok to keep whole  (note, these are from old by-precinct-name method)\n",
    "Canton 70,872 -- I AM OVERCOUNTING, but ok to keep whole\n",
    "Youngstown 67,296 -- I am undercounting (60,059)\n",
    "Lorain 63,468  -- about right\n",
    "Hamilton 61,932  -- about right\n",
    "Green 60,424 - about right  #Census has several CDP's inside Green\n",
    "West 8 = West Chester 64,901 -- about right\n",
    "not Springfield 57,719\n",
    "not Fairfield 42,000 -- we split out Fairfield township :-)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "78564573-ce6d-4d0e-b2d4-3c33f25b0be6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pull in the any-split Home Districts ...\n",
      "Now convert (approximately) to full VTD lists. We didn't store the VTDfrag lists, so use binary in-out if frac > 0.5\n",
      "Lets compare our true HDpops to this partial-based calculation\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#10/27/24\n",
    "print(\"Pull in the any-split Home Districts ...\")\n",
    "infile = \"./2024state_HD_output/OH8941VRApatchedVTDs.csv\" #OH8941legisl60_1p5PatchedVTDs.csv\"\n",
    "HDdf = pd.read_csv(infile)\n",
    "HDvtdListString, partialString, fracString = HDdf[\"HDvtdList\"], HDdf[\"partials\"],HDdf[\"HDvtdFrac\"]\n",
    "HDCPx, HDCPy, HDwt = HDdf[\"centroid x\"],HDdf[\"centroid y\"], HDdf[\"HDweight\"]\n",
    "HDvTruePop = HDdf[\"HDvPop\"]\n",
    "HDCP = [Point(HDCPx[v], HDCPy[v]) for v in range(nVTDs)]\n",
    "hdCP = HDCP.copy() #nomenclature\n",
    "nHDs = len(HDvtdListString)\n",
    "HDvtdList = [ast.literal_eval(HDvtdListString[h]) for h in range(nHDs)]\n",
    "HDpartials = [ast.literal_eval(partialString[h]) for h in range(nHDs)]\n",
    "HDvtdFrac = [ast.literal_eval(fracString[h]) for h in range(nHDs)]\n",
    "print(\"Now convert (approximately) to full VTD lists. We didn't store the VTDfrag lists, so use binary in-out if frac > 0.5\")\n",
    "for h in range(nHDs):\n",
    "    for i, frac in enumerate(HDvtdFrac[h]):\n",
    "        if frac >= 0.5:\n",
    "            HDvtdList[h].append(HDpartials[h][i])\n",
    "print(\"Lets compare our true HDpops to this partial-based calculation\")\n",
    "HDvtdPop = [np.sum ([ tractPop[v] for v in HDvtdList[h] ] ) for h in range(nHDs) ]\n",
    "popRatio = [(HDvtdPop[h] + 0.0001) / (HDvTruePop[h] + 0.0001) for h in range(nHDs) ]\n",
    "plt.scatter([h for h in range(nHDs)], popRatio, label=\"converted to true pop\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "#HDvtdFracList  = [list() for h in range(nHDs)]  ##modified 11/24 - do not use VTD fragments\n",
    "#for h in range(nHDs):\n",
    "#    for v in HDvtdList[h]:\n",
    "#        for f in VTDchildren[v]:\n",
    "#            HDvtdFracList[h].append(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "1fbef9d8-35f8-49cf-bb3e-6d6acf517033",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8941 786629.8666666667\n",
      "647\n"
     ]
    }
   ],
   "source": [
    "print(nHDs, aDP)\n",
    "print(np.min(unitPop))\n",
    "minDistrictPop, maxDistrictPop = 0.995*aDP, 1.005*aDP  #0.95, 1.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b08d6302-63ee-451f-ae11-d61c5440f425",
   "metadata": {},
   "outputs": [],
   "source": [
    "unitVTDlist = [unitTractList[u].copy() for u in range(nUnits) ] #nomenclature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "9fe8940f-434d-405d-b52f-e17074715fcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now snap each HD to units.  Each HD is assigned its home unit, then we build out whole units, then ...\n",
      "... we add units that were partially included, biasing toward close, mostly full ones\n",
      "working on snapping HD 0 in unit 334 to whole units\n",
      "working on snapping HD 2002 in unit 194 to whole units\n",
      "working on snapping HD 4004 in unit 165 to whole units\n",
      "working on snapping HD 6006 in unit 408 to whole units\n",
      "working on snapping HD 8006 in unit 508 to whole units\n"
     ]
    }
   ],
   "source": [
    "#10/27/24\n",
    "print(\"Now snap each HD to units.  Each HD is assigned its home unit, then we build out whole units, then ...\")\n",
    "print(\"... we add units that were partially included, biasing toward close, mostly full ones\")\n",
    "minSnapFrac, minEligFrac = 0.667, 0.1 #we auto-add contig munis at least this full in HDvtdList\n",
    "homeU, HDvPop, HDunitList = [-888]*nHDs, [0.]*nHDs, [list() for h in range(nHDs) ]\n",
    "popHDlist = list()\n",
    "for u in range(nUnits):\n",
    "    origArea = np.sum( [tractArea[v] for v in unitVTDlist[u] ] )\n",
    "    origL = origArea ** 0.5  #characteristic local district diameter\n",
    "    for v in unitVTDlist[u]:\n",
    "        homeU[v] = u\n",
    "for v in range(nHDs):\n",
    "    if HDvTruePop[v] > 0:\n",
    "        popHDlist.append(v)\n",
    "        \n",
    "for nh,h in enumerate(popHDlist):\n",
    "    if nh%2000 == 0:\n",
    "        print(\"working on snapping HD\",h,\"in unit\",homeU[h],\"to whole units\")\n",
    "    currPop, addedUset = unitPop[homeU[h]], { homeU[h] }\n",
    "    useFrac = [0.]*nUnits\n",
    "    for v in HDvtdList[h]:\n",
    "        useFrac[homeU[v]] += tractPop[v] / unitPop[homeU[v]]\n",
    "    shouldAddUset, hasFracUset = set(), set()\n",
    "    for u in range(nUnits):\n",
    "        if useFrac[u] > minSnapFrac :  #munis more than 2/3 captured should be snapped if contig path found\n",
    "            shouldAddUset.add(u)\n",
    "        if useFrac[u] > minEligFrac :  #munis that were at least 10% captured are eligible to be snapped\n",
    "            hasFracUset.add(u)\n",
    "    shouldAddUset = shouldAddUset.difference(addedUset)  #at this point, this knocks out just the home muni\n",
    "    nJustAdded = 1\n",
    "    while nJustAdded > 0 and currPop < aDP:  #this block -- add all mostly full HDs that are contig to growing list\n",
    "        nJustAdded = 0\n",
    "        canAddUset = set()\n",
    "        for u in addedUset:  #building all neighbors of in-HD munis\n",
    "            canAddUset = canAddUset.union(set(unitNbrs[u]) )\n",
    "        canAddUset = canAddUset.intersection( shouldAddUset.difference(addedUset) ) #whittling to eligible list\n",
    "        for u in canAddUset:\n",
    "            if currPop + 0.8 * unitPop[u] < maxDistrictPop: #prevent big overadds.  0.8, 0.9 is adjustable\n",
    "                currPop += unitPop[u]\n",
    "                addedUset.add(u)\n",
    "                nJustAdded +=1\n",
    "            if currPop > minDistrictPop:  #it's OK if we go over; we'll trim back later\n",
    "                break\n",
    "    # OK, we've added the 2/3 full munis.  Now add more contiguous partials if needed\n",
    "    eligibleSet = hasFracUset.difference(addedUset)\n",
    "    for u in list(eligibleSet):\n",
    "        if currPop + 0.9 * unitPop[u] > maxDistrictPop:\n",
    "            eligibleSet.remove(u)\n",
    "    contigAddableSet = set()  #the eligible units adjacent to the current (growing) set of units\n",
    "    for u in addedUset:\n",
    "        contigAddableSet = contigAddableSet.union(set(unitNbrs[u]).intersection(eligibleSet))\n",
    "    while currPop < minDistrictPop and len(contigAddableSet) > 0:  #add close, mostly used munis\n",
    "        canAddUlist = list(contigAddableSet)\n",
    "        canAddScores = [ unitCP[u].distance(HDCP[h])/origL * (1. - useFrac[u]) for u in canAddUlist]\n",
    "        addIdx = canAddScores.index(np.min(canAddScores))\n",
    "        u = canAddUlist[addIdx]\n",
    "        currPop += unitPop[u]\n",
    "        addedUset.add(u)\n",
    "        contigAddableSet.remove(u)\n",
    "        eligibleSet.remove(u)\n",
    "        contigAddableSet = contigAddableSet.union(set(unitNbrs[u]).intersection(eligibleSet))\n",
    "        for u in list(contigAddableSet):\n",
    "            if currPop + 0.9 * unitPop[u] > maxDistrictPop: #prevent big overadds\n",
    "                contigAddableSet.remove(u)\n",
    "    if currPop < 0.2 * aDP:\n",
    "        print(\"HD\",h,\" has only\",r3(currPop/aDP),\"pop/aDP. We will swell by nearest munis to shore it up\")\n",
    "        canAddUset = set(unitNbrs[homeU[h]])\n",
    "        for u in addedUset:\n",
    "            canAddUset = canAddUset.intersection(unitNbrs[u])\n",
    "        canAddUset = canAddUset.difference(addedUset)\n",
    "        while currPop < minDistrictPop and len(canAddUset) > 0:  #add close munis, biasing towards highly used\n",
    "            canAddUlist = list(canAddUset)\n",
    "            canAddScores = [ unitCP[u].distance(HDCP[h])/origL * (1. - useFrac[u]) for u in canAddUlist]\n",
    "            addIdx = canAddScores.index(np.min(canAddScores))\n",
    "            u = canAddUlist[addIdx]\n",
    "            currPop += unitPop[u]\n",
    "            addedUset.add(u)\n",
    "            canAddUset.remove(u)\n",
    "            for uu in unitNbrs[u]:\n",
    "                if useFrac[uu] > 0 and uu not in addedUset:\n",
    "                    canAddUset.add(uu)\n",
    "            for u in list(canAddUset):\n",
    "                if currPop + 0.9 * unitPop[u] > maxDistrictPop: #prevent big overadds\n",
    "                    canAddUset.remove(u)\n",
    "\n",
    "    HDvPop[h] = currPop\n",
    "    HDunitList[h] = list(addedUset)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "6ca0e286-f4bc-4153-8e53-42e5b6757339",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets plot our unit-snapped HD pops vs. orig pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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Dhg0AGiK5QYMGISMjAzk5OTh8+DDGjh2LiIgITJgwAQCwZ88ejBw5EgsWLMC9996LDRs2YOjQoTh48CCSk5Ml14UcyxGJ9DxpBpAjm9AdOT7lidvaYUn+KYftz9VO/34F9XoBy3aexLu7S1F59Zq4TROmxtz7u2NgkgbLdp6ymfSxrLIaE9cdRERIoNntGj7tO4WzB6jbO2HAmo+KfvWI6wp5FlldY/feey9iY2OxatUq8XfDhg1DcHAw1q1bB0EQEB8fj2effRZ/+9vfAACVlZWIjY3FmjVrMGLECBw7dgxJSUk4cOAAevfuDQDIzc3FPffcg19++QXx8fFYsWIFXnjhBWi1WqhUKgDAzJkzsWXLFhw/fhwAMHz4cFRVVWHr1q1iXfr27YvU1FTk5ORIqost7Bq7zpV96gUlFzFy5V6b5eR2r8jl6K45qZ/LGgWAUHULBCiAyuprNst7shBVAK5YaUkKDlTiap2+2e/z1iM9cc/N9gVBHEti2cdFv+KZjUU2yy0dkYoHUm+QtW9bXW5TMzpj8Q7prftyup2dfV0h13Ba19htt92G/Px8/PhjwxPaDz/8gF27dmHIkCEAgNLSUmi1WmRkZIivCQ8PR1paGgoKCgAABQUFiIiIEIMgAMjIyIBSqcS+ffvEMnfccYcYBAFAZmYmTpw4gUuXLolljN/HUMbwPlLq0lhNTQ10Op3Jj7ez1e0gpWzjrglDd4SzmpE9YQaQ3K45KcfZMLuuObdRAcDlmmteHwQBsBoEAXBIEKQAMH+b+e4ZuV1yzj7vvY2zBqhL6XJbLSP9g+H79kBqvKTynFnof2R1jc2cORM6nQ5du3ZFQEAA6uvr8corr2DUqFEAAK1WCwCIjY01eV1sbKy4TavVIiYmxrQSLVogKirKpExiYmKTfRi2RUZGQqvV2nwfW3VpbMGCBZg3b56EI+Ed5LRoWCp7/y1xePub0mZNSTXM6th96necq7iKGyKCcVunaPTt0PDU1fiJ290zgOR2zVk6drOzuiEyVG3y2SzNriPnaNw9Y2jh2XFUi4+KfkV5VZ1Y1vhvlndUazbXkqdMxfaElqpe7SIRFRpocgyN2UqbYekz7C25aLPLreKq+fc0x9A1Gh6skpQ/izML/Y+sQGjTpk1Yv349NmzYgO7du6OoqAhTp05FfHw8Ro8e7aw6usysWbMwffp08d86nQ4JCQlurJH9rE3TnrjuIKZldEb76FDEtArCpapaTN7QtGxZZbXFnDDGgyEb96nXXtPj/wpO40z5FVypuYYvjmqhqzZ9+l/+VQlCVQEIbKFExZXrF7Wo0EDcd3McFArAWqetUtFwIXYGKRdiw8218mqt2eNcVlmNpzZ8b/K7qNBAPJh6A7JS4rD1MFsVXOm9/WfwwkeHcObiFdRbOK/M/c0aMw6EB3SNReGZSy4PRiwH3kmIDFW5pD6GOlgLggDLA9TNfYaoUBV6JIRj70/lkuoQERyIyqt1Fh8oIkICsXxkT/Tt2BoBSgW2H7L9nQtVBeBavR611/RO/dt6QiBL18kKhGbMmIGZM2eK42tSUlJw5swZLFiwAKNHj4ZG05C2//z584iLu/60dP78eaSmpgIANBoNLly4YLLfa9euoby8XHy9RqPB+fPnTcoY/m2rjPF2W3VpTK1WQ61WSzsYTtTcL0m9XsDcT45YbVo27l9XKuxvnSirrMazm4rwp94J6NuhNRblHsPKb0shZaJIVW090Kh7pLyqDmsLztp8rV4A3sz/Ef06tRGfOJt7zAwtBRsP/CzpNVt/+BWfHCqTfOzKq+qwyoEZnUm6T35wXOBpCIRvmfe5SfddUKASXWNbIuvmGzD6tvYNrRs/XURByUUAAtI7RIs3ZXtZesBpCOIOmvzOWVPCLdXBmACgZ0I4WqkDUa8XmmRNN/f68qpa5B//TXI9MrrF4IODv5rNXSYAaBcVgs2FP0OvF5DWsTXmb7O9yHBVbT0ee3d/k99rwtQY2aet+PAoZ3Hj/aXl0OqqUX65BlGhKpwtv4r39p+FVnc9CIwIDsSYfomYMqCTuF9z9wGgedc5Mk9WIHTlyhUolabDigICAqDXN1wMEhMTodFokJ+fLwYbOp0O+/btw6RJkwAA6enpqKioQGFhIXr16gUA2LlzJ/R6PdLS0sQyL7zwAurq6hAY2DATJC8vD126dEFkZKRYJj8/H1OnThXrkpeXh/T0dMl1cafGJ3mvdpEoPHPJYpO98QXNVqC0bOcpaHVN13+ypBmzWwE0LJ+wpehc83Zih2VflmDZlyUIVQeghQKoNGp1Msw8knITkJuLxGD9fmkBE/mmxmOYquv0KPpFh6JfdHhl+7Em5Zd9WYIWSiApLgxhQYFIuTEc/Tu1MRscWboJypkpZZhN58hM3g0PWdLqcPDnSoxatQ9BLZQY2ScBg7rHoVe7SMmvt+W/B39FcGDD/cj4b2HY9w+/VOKHXyqxpegclACaM+JMq6sxeXiMClVhaGo8BiZpxGu34W+VmhCBDfvO4JuTv+Hg2Qr8IWE8X8XVOize8SPe/qYEI25NQKugQKwtOI1LRq3lLdUtEKCEyQzL8KAWePy29ggLCsTPl66gXVQIHktvD1WLhuNi3DrfeJuxhrUBS3Hg9CWEBCqRFB+ONmFBiGnZNGeXLwZesmaNPfHEE9ixYwf+/e9/o3v37vj+++8xYcIEjB07FgsXLgTQMMX+tddeM5myfujQIZMp60OGDMH58+eRk5MjTp/v3bu3OH2+srISXbp0waBBg5CdnY3i4mKMHTsWixcvNpk+///+3//Da6+9hqysLGzcuBGvvvqqyfR5KXWxxlmzxnKLyzD3k6MmTwRKheWAxDg5GQCbyzNMXHfQzF7801uP9GzSXQBcf6o6/XuVrNknRI4WERKI1x5KEYMVc4F5q6AA9OsYjdwj5y3txqKW6haY/0B3aMKDm30jW7rjx2Z9X5obkHgiTxvvp1QA429vGGPbuHVeoQDuvKkN+neKRlRLNTRhQdh5XIt3vj0t6TPIfSh3Jzn3b1mB0B9//IHZs2fjo48+woULFxAfH4+RI0dizpw54gwvQRDw0ksv4e2330ZFRQX69++Pt956CzfddJO4n/LyckyZMgWffvoplEolhg0bhjfffBMtW7YUyxw6dAiTJ0/GgQMHEB0djaeffhrZ2dkm9dm8eTNefPFFnD59Gp07d8aiRYtwzz33iNul1MUaZwRC9gYqCgDhIYGovNK0T9zwRRzTrz3e23cW1dd87VJjv8YXKUOuGeNxSUSe4K1HeuDkhctODcyb0122/VBZk+438i9yHsrdzWmBkL9xdCBUrxfQ6+95vAkTkVvYWvpCHNNSeRXlVbWIDFHh0pVanC2vwn8KznpUywe5T0t1AC7XNE1/YXjwNJ6M465WIjn3by666kJ7f7rIIIiI3MYQyMz84DBaBQWib4fr45O2HzqHFz8utjgTjMjAXBAEmJ+MY5wN3FO70dgiZIWjW4QmrSvEZ8XmcxgREblaVKgKf38gGT/8csliqgwiR2ipboHLNdcHejs75QO7xhzEkYFQvV7AzXM/l704JRERkT9w5Bgjdo052JXaa2hR23QKpFKhQFBggEk5Sw6UXmIQREREZIEh5cPSEbdgYJJG/H0LpVKc9q/XC6i+ZvlealxWKgZCEvR5JR9KdUiT39/VpQ1Wj+kj/rvX/B24Wmf+D9SpTajT6kdEROQrntn4A4Afrv/77s6YNrBhtvep3y5j0OJvLL52wh0d8Pw93WS9n7ywiewWKDNCJSIiIufjGCErDH2MZb9dNNvHKKdrTBCAjH9+DW1lNaegEhERWbHo4RTce3M8APu6xjhGyMFCVC0QorJ9qGyV4crjRETkzSzdv3okROD7nysc9j4JkaFm76lKpULS/VgO9te40ODkOKx4tCc04abLe6gCPCOXAhER+R9Dxn1r3nqkB3LM3L/iwoOQ82hPPDe4q0PqovjfPg3LIbkCW4RcbHBynJhY6p1vS7DzxG+orWf7kCcJVSlRVctlSsi3BAUqUV3H85qaeu2hFADAzA8PN0n623gtPEuJEev1AuLCg2QvXm3M0CTw0n1JLk22yEDIDQKUCnx14jzyj//m7qr4vMiQQLwyNAXztx21Oj5LqQBGp7fDoO5x6JMYhc+LtVxXiXxGVGgg9s7KwI6jWmaPJpG5IGfvTxdRUHIRgID0DtHo27G1SVASoFQgvWPrJvsKUCrE4R+A+e6zluoADO+dgIwkDS5V1WD+tmMmgZPGTWuVcbC0Fc5afb72mh5dZ39mcbV5uYIClRAEoIaLrYqGJGvwaN924hICucVlVr+gbz3SA/f8b2CeQW5xGbI/OITKq5YHwRN5ksbjN8ytLWa8Yvjp36/gvf1nodVdvxkpFTC5NrVUt4BeEHCFedAQFx6E+2+Jw//tPes1x6OlugVaKBWouHo9+I0ICcSY2xIxZUAnh7e85BaXNVmMNSI4EGP6tceUAZ1N3s+Zq9czs7SDOCsQWvXtT5i/7Ziksi3VAdALMPnSRYWqkJYYiY5tWiG9Y2v07dAQnRufUOai7YjgQNzdtQ0++P6cwz6Lp7GWmdTcF9RWJtN6vSA+If1y6Qq2FPnusSPvZDiHAftWA298M+rVLhKFZy6Z3JyA69eX6FA1nlpfiMpq731AULdQSn5wjAxpgYd63IiMJI1JN9CeU7/jXztP4odfKk325YzJMCEqJa4Yddc3DlYbCwtqgZfu6474iOAmfz9XrPPlzABHKgZCDuKsQGjOx8X4T8EZm+UGJcVixaO9ANh3Eps7GbceOodnNhbZXfceN4YhSB2A85U1+On3K3bvxxFaqltgwUMpiG6plnxsmvMF/bjo12YdO/JuQYFK3HeLBqW/XYFeLyAyVAWFQoEdxy7YfG2oSoGU+DC0Clbj7m4x2H5YiwOny3HVaMxOq6AAjLmtPd7cWWJzf4/1bYuebSOhCQ82OYdddQPKLS7DxHX2dR1HhQaiT/vWyD1ie93FB1PjUVVbjy+OnrdZNjo0EMGqFvj50lWbZdePS4NSqWgI7FqqAQH4varGYiAo55rSq10kDpSW4//2liL/2G+oaxSxBAUqMbx3Am6MDEHFlVooFEB6h2hcqqrBnE+PmHRbRoUG4u8PJCMzOa7Rg24tJm9o2sJtrgXQX3H6vIdrF9U0S7U5aUZfQHN9sraY68uNaRVkobQ0995yA6JbqRHdUo2n1n2Hymr3NQ+3VAfgnpQ4WRd6S/3bUjT32JF3q67T46EebU3On3q9gH6v7TTpWjKnVZAK6yf0E8/VEX3amQ1aAGBz4a8Wx7Mp0DCOYu79yWbP++ac33IMTo5DzqM98dLHxTj/R63N8lGhgXgw9QaxVWXroXOSAqEvjp7H24/1lhQI/euRXuiTGIV+r+VDq6sxW8Zw/Pp2bNqKfu/N8XZdb80d836do9Gvc7RJi7KlMTfGhtwcbzGQbfweK5Q9m7QAumuMjbdjIOQGj6W3xyvbj1lt2lQqGso5Wp/EKMSFB9mV2FGpgOQuPVfQ6mqwv7TcJRd+oHnHjjxbqCpA0lqAF/4wDXgClAqM7NMWi3f8aPV15s5VS0GLpXxj7ppRY4lhBuyynaesfv5pGZ2bjA2R+lBRVVsPvSBY/d4ZghtD0DD3/u5mxwMaH7+8o1q7uhHlClAq0K9TNPp1ipZcXur1zHgGsju7oHwB8wi5gaqFEuNvT7RaZvztibIXjpPCMLIfuH5hkMpRg7sdqfGNyZmac+zIcynQsD6RFOZu4O2jpbXwSj1XLeUb04QHeVyXR4BSgWcyOiPn0Z6Is5Bf5pmMm5rcnPskRiFUHQAp9v500eL3zlxwaOv4AcCkdQebTPPWVlZj0rqDyC0uk1QvT2AInB5IvQHpVlqayDqOEbLCWWOEDBZsP4qV35aaBBhKRUMQNOueJIe/n7Hc4jKzOSO8zXvj+7qsRcjA3KBr8k6GVoCBSRr0X7jTZqvDruwBTW42BSUXMXLlXpvvJfdc9YQBp3LIre+kdd/hs2LbXV6hqgC88edbAMgbDG6p67H/wp0Wv7vW/s7kXThGyEvMuicJzw7qiv8rOI0z5VfQLioEj6W3d0pLkDneHAQZN4e7mnGTtLbyKuZ8cgR/ePEMGn80+a6O6N+pjcnN2t4uKVtdpvaeq64a7+MoUutrCFASo0Ml7beqth6T1h3Eikd7Ylf2AMnBlrn6FJRctPoAIwAoq6x2aZc7uR8DITdTtVBi3O3SmuUdpV4vYN6nR522f8ONJCIk0Gyw1TpUhYtVtgdYWts/4N6xEoaLbEHJRQZBXiYyJBDTB3Zpcu4YulTkDkA1TiTn6eN63K05ranzPj2KgUmaZgUoUrsnXdnlTu7HQMgP7S8tl3QhigoNxOj09li846Ss/WuMuhsMrSblVbWIaqmGJqxheun/e/1Lm3VQoCGYUrdQmswC8aSZEbxgep8FD6VYDErsHYBqbxDlTwxJTe0Zi+Golhqpg7Q5Q9S/MBDyQ1Jv3rPv7Y77b4nHxgM/W50pFRUaiKV/7oHyq7U2p3waGJ6grV0UBQAP9bgBA7rFmuT58KSxEnIumPemxGLrYdtjIsh5xvZrbzMosbdLirN4LDO0Qjd3QGpzHzyc1Y1J3o2zxvyQ1Ju3JixI0kyp8qo6PPfhIahbKCXPXDA8QTeeaWJg2MOq3acx6p19eHZzkaz9u4rhwmrL0uGpWDqyFzRhahfUiiwZmKRx6v45i8c8qa3QtjS3pcba9YzdmP6LgZAf6pMYhYiQQKtlIkMCxaciS9NRjdkz9XRwchx2ZQ/Ae+P7YvGfb8HsrG4Y0689gKYp6rW6Gkz0wKmthgurtcvmX+5IxAM9bkDeUS2quR6cWyjQMMOIT/ru0dyWHEf+/bwpPQG5BrvGyKzGgcjg5DgM6BqLvgvyUW5moLOAhouVYUCj1Ccq426Ier2AXn/Ps1p+5oeHZe3fFSyND2kdqsL8B5Jxz81xzRofQdaNvz0Rd3aJwbq9Z/BZcdNsxXzSdz85LTnm1uoSAIy4NcFh9WE3JhljIOSH9peW25w6X3GlrsnAxMIzl8wGQQbNHdC4t+SipHrtLbmIfp2lZWp1FWsXVkeNjyBTjXNu9esUbXZWkjsGLHtbDiBnkzo2Z3ZWEuZvMz+rbPGOk9h44GeH/S29LT0BOQ8DIT9k7xRSZ089Lfjpd8nlPC0QAixfWB01PsIXGZ7+5a7Y/XDPG/DqQzc3ybnlCU/65oIxZyzf4E2kphgYnByHzGTLy3YYuuDZhUWOxDFCfsjeKaTOn3oq9WblXU/W9gaGCjS0eviyiJBATMvoLLu1bHfJRZuJ9NwxYNnQBeoLyzc4mpyxORsPnDW7D8N5Mu/To6j3xDV/yCuxRcjHmWuit3cKqbOnnqZ3bI1lX56SVM6b2BMYGm7d429PxNvflPpst5q6hRJtW0vLMGzME7P/WusCtXcMna+R0mJnqwWV2Z/J0RgI+TBrTfT2ZMJ1dgbdvh1aW8xGbRAZEoi+Hbzr4idl1XqlwnRRW+OklCGqFnj7259QVWN7dXRvo9XVoPxyje2CZnhaMkvewKWxNTaH2Z/J1dg15qNsNdEDsGsKqTOnngYoFXjtoRSrZaxlBfZUtnKXKAAsG9kT743vi6UjUvHe+L7YlT0AQMMCkYt3nPTJIMggKlQlKRdTY56W/Zc3cMfwluzP9XoBBSUX8XHRrygouciuOi/GFiEfJLWJflf2ALuXE3DWgNTByXHIebQn5n5yFFqd7ww2lbsEgz9Nt9eEB0vKNG4sKjRQdhess2dyecsN3NN5Q/ZnDoj3LbJahNq3bw+FQtHkZ/LkyQCAO++8s8m2iRMnmuzj7NmzyMrKQkhICGJiYjBjxgxcu2a6aOVXX32Fnj17Qq1Wo1OnTlizZk2Tuixfvhzt27dHUFAQ0tLSsH//fpPt1dXVmDx5Mlq3bo2WLVti2LBhOH/eP5Y3kNNEb21gqbUnHmcOSB2cHIfdMwc0aSHx9guMcQJJa5/LX6bbGyfJE1saw6QFCX9/IFnWOZdbXIb+C3di5Mq9eGZjEUau3Iv+C3c6dPCy4QZuqVZM6iiNp2d/5oB43yOrRejAgQOor7/eRF9cXIyBAwfiT3/6k/i78ePH4+WXXxb/HRISIv5/fX09srKyoNFosGfPHpSVleHxxx9HYGAgXn31VQBAaWkpsrKyMHHiRKxfvx75+fl48sknERcXh8zMTADA+++/j+nTpyMnJwdpaWlYsmQJMjMzceLECcTExAAApk2bhm3btmHz5s0IDw/HlClT8NBDD2H37t12HCbv4ogmenc/8fhqjg8pn8ufptsb39AGJmnQSh2I9ftOY3ux9YcWpcwgyFxrk6OnYnMVesfx1EVsOSDeNykEQbD7wXPq1KnYunUrTp48CYVCgTvvvBOpqalYsmSJ2fKfffYZ7r33Xpw7dw6xsbEAgJycHGRnZ+O3336DSqVCdnY2tm3bhuLiYvF1I0aMQEVFBXJzcwEAaWlpuPXWW7Fs2TIAgF6vR0JCAp5++mnMnDkTlZWVaNOmDTZs2ICHH34YAHD8+HF069YNBQUF6Nu3r6TPp9PpEB4ejsrKSoSFhdl7mFyuoOQiRq7ca7Pce+P7mr0pW7pxGL7WzOHhXB8X/YpnNha5uxpO1TgZornA2xJD18iu7AE2bzb1egH9F+60uF85+5LK3Q8RvsTTElM299pKriPn/m33YOna2lqsW7cOY8eOhUJx/cRcv349oqOjkZycjFmzZuHKlSvitoKCAqSkpIhBEABkZmZCp9PhyJEjYpmMjAyT98rMzERBQYH4voWFhSZllEolMjIyxDKFhYWoq6szKdO1a1e0bdtWLGNOTU0NdDqdyY83ak4Tva0nHoA5PJzNH8aQCALw9jelyC0us9jVYPG1uN61a4ucbmJHkdoFSrZ52iK2HBDvm+weLL1lyxZUVFTgiSeeEH/3yCOPoF27doiPj8ehQ4eQnZ2NEydO4MMPPwQAaLVakyAIgPhvrVZrtYxOp8PVq1dx6dIl1NfXmy1z/PhxcR8qlQoRERFNyhjex5wFCxZg3rx50g+Ch2pOEz2nALufIZD15e4x424EQRDsGg8l5WYj9Ya0+9RvDm118NWuXTk8rTXHETgg3jfZHQitWrUKQ4YMQXx8vPi7CRMmiP+fkpKCuLg43H333SgpKUHHjh2bV1MXmDVrFqZPny7+W6fTISHBcQv9uZK9fezNeeLxxQufOwQoFbj/ljj8+5tSd1fFqQxBtb2k3Gyk3pCWfVki/j+7sayT8j231D04OysJkaEqr71GeMOMNnv4+7XbrkDozJkz2LFjh9jSY0laWhoA4NSpU+jYsSM0Gk2T2V2GmVwajUb8b+PZXefPn0dYWBiCg4MREBCAgIAAs2WM91FbW4uKigqTViHjMuao1Wqo1Wqrn8mb2DPN3d4nHjnjIvz9S2dLvV7AJz9w5oklcm42UpJZNsb1rCyT8j23NMawrLIaT204aPI7bws6fXFAPMe02TlGaPXq1YiJiUFWVpbVckVFRQCAuLiGg5meno7Dhw/jwoULYpm8vDyEhYUhKSlJLJOfn2+yn7y8PKSnpwMAVCoVevXqZVJGr9cjPz9fLNOrVy8EBgaalDlx4gTOnj0rlvEXcvvY7RlfJGc6qSumMXs7f5o1Zg8B0m821qZiW9s/wLFwjUn5nstN/eCNU86dmVTW1ZgKoIHsQEiv12P16tUYPXo0WrS43qBUUlKC+fPno7CwEKdPn8Ynn3yCxx9/HHfccQduvvlmAMCgQYOQlJSExx57DD/88AM+//xzvPjii5g8ebLYEjNx4kT89NNPeO6553D8+HG89dZb2LRpE6ZNmya+1/Tp07Fy5UqsXbsWx44dw6RJk1BVVYUxY8YAAMLDwzFu3DhMnz4dX375JQoLCzFmzBikp6dLnjHmr+Tm8JAzuJpfOmncOdAyOFCJx/u6pjvYEFRrwtSyltGdendnWTcbSzcua5wxiNqbSf2e7/3poqwg3luDTl8YEM+JMdfJ7hrbsWMHzp49i7Fjx5r8XqVSYceOHViyZAmqqqqQkJCAYcOG4cUXXxTLBAQEYOvWrZg0aRLS09MRGhqK0aNHm+QdSkxMxLZt2zBt2jQsXboUN954I9555x0xhxAADB8+HL/99hvmzJkDrVaL1NRU5ObmmgygXrx4MZRKJYYNG4aamhpkZmbirbfekvtx/ZKc8UVSB1fv/eki829I5M6Bllfr9PjP3p+d/j7GQTUAcdkXKdbsOY2uca1kB0PG3cQnz1+WtMAvZ/80kPo9Lyi5KHvf3joBw9sHxHNizHWyA6FBgwbBXOqhhIQEfP311zZf365dO2zfvt1qmTvvvBPff/+91TJTpkzBlClTLG4PCgrC8uXLsXz5cpt1oqakji+SeqMoKLH+pOhPXzpb+iRGISI4EBVXLS8+6+0aB9UrHu2J5z86jPIq25+54mqdXWN4jG9cBSUXJQVCnP3TQHpAaH/rAYNO12IqgOu46CpZJGV8kfQbhbQL5GfFZX6/gGGAUoEx/dpLKqvwssazUHUA1j+Z1qQbYXByHPbOykBUqEryvprTbM/lMOSR+j1P7xBt9bg64j3IMZgK4DoGQiRL4/XHerWLlHRDSe8QLWn//yk4wwHUAKYM6IyIkECb5ezPC+8eVTX1UCoUZoNqVQslXn0wWdJNtLljeDx9PStPIzVw7NuxtezB6Qw63YMPA9cxECLJzM34+n+vf4n7b2l4srd2Q+nbsbWsJ0V/H0AdoFTgtYdSJB+vluoWiG3lHakfrDW1G8anRQTbDgJt7csWX5r942xyAkc5g9NdGXRaW0TaH/Fh4LpmrTXm67x1rTFnsLX+2IQ7EvHJD2WS8osA0jrKnLEOlCs5Il9SbnEZnv+oGOVVtTbLRgYH4u5usQhSKVGv1+OTojJU1dbbfJ2rSVmHaffJ3zFq1T6H7MsW5rWSrjn5wi5V1WD+tmNuyVfDXDmW+eqxkXP/ZiBkBQOhBlIXrvx6xl0oPHNJdsZZW7xxAUNHXFwMN5Jth89h3d6zzqqqy731SE/cc7P1Y2A452xl8PXWINmbNSdwdEfQyUWkbfPFhwEGQg7CQKiBo1dcNnzpPisuw38KztgsP+Wujpg2sIvXfDEdceG1J2D0FnESAxhLLYi8gZFUUh/iGFD7HpesPk/+w9HTLA2z0YZIvIkt+7LEawZPOyJJmdzV2L2N1EHOHMNDzSUnVw75L7sXXSX/4axplnLWgfLU9Z8aNynrBaFZ+ZLkLlHgraQGzfasl0dkwFw5JAUDIbLJWSsuW1vAsDFPzD5trvuqubOd/GWdMTlBs7dn8CX3Ya4ckoJdY2STM6dZyplq60nN2Ja6r6Rmg7Z04fWHJ1N/yU3ii7xtCjpz5ZAUbBEiSeSsP2bPvgcmabA470evWP+pOd1XtlrP/OHJdHaWf+Qm8TXeOM3aWquzv+XKIcsYCJFkzhyvEaBUoF+naK9Y/8ne7ispF15/WGcsUsYyGuQZLM2EdMbYPUdP5XbmQxz5BgZCJIszx2s4ayySIxhfnE+e/0PSaxoHNFIuvIZ1xhbvONnsOnsqd7fokTy2ZkI6cuyes1qdOOierGEgRB7DU5ux7c3ps/yRnlAqFbIvvFMGdMbqPadRccU3W4Xc3aJH8siZgt6chyRntzpx0D1ZwsHS5FE8LXeMPTl9jBegTO/YGg+k3oD0jq0lB3BS1hkLCvTOr27rUBV6tYt0dzVIBldMQXdE/i0ie7FFiDyOpzRj2zMo2lEtV5bGNRhU1+nt3rc7Xayqxf97/UuOzfAirpiC7qpWJyJzvPOxknyeoRlbbmuKI9kzKNqRLVeDk+OwK3sApmV0bva+PImhq8MbMoWTa6agM/EhuRNbhIgskHrRnXJXJ3SObem0lquNB3526P7kCFEF4IqDV7D3xOSYZJkrxu4x8SG5E1uEiCyQetHt1ynaaS1X7s407eggyMCTkmOSbc4eu8fEh+RObBEissATpvP7eldA3lEtx3x4CWfnEfPEGaPkH9giRB7BE1P3O3NpEal8vSvg3d2nXTpWyBPPM2/izLF7njZjlPyHQhAEXgks0Ol0CA8PR2VlJcLCwtxdHZ/l6an73Vm/er2A/gt3WmyV8naGVrVd2QOc/rTv6ecZNXB0ZmnyT3Lu3wyErGAg5HyWkqgZLnue8iTozouz4RgB8KpgaEiyBp8VayWVfW98X6d2kTX3POPN2TV4nMlR5Ny/OUaI3EZKErWZHxxGq6BA9O3gnin0Bu7MSmsrp5AcLZQKXHNRd9Dj6e0RHx6EVbtP2yxrPBZK7s3QVvnmLhHBliTX4HEmd2EgRG4jZUZUxdU6jHpnn99fEM0NVL3wRzWe2Vgkaz+uCoIMGaT3l16UVN4wFmr7oTK8+HExyqtqxW3W/vZSbp7NSdbnysVGfU3jALVXu0gUnrlkNmDlcSZ3YiBEbiNnRhQviE1bpVZ9+5Mba2PdxapapL78hc3p98Yz7xZsP4p/f1PapEyZhb+91Junvcn67G1JYveO+QBVqQCM43BDwDowSeOyRV2JzGEgRG4jZ0YUL4hNnSm/4u4qWCU1B9FL9yXh8+Iys0GQgQDTv72cIMXeZH32tCSxe8dygNq4MdIQsE7NuInLa5Bbcfo8uY2tJGqNMQmfqXZRIe6uQrNNzbgJA5M0ePHjYptljf/2coIUe5P1yW1JsrRArz8tKSJnfT5DmdV7LAfAxnw9pxa5DwMhchtreXqs8fcLoiEXTkRwIBRe3jBWV6/H3p8uoryqTlJ5w99eTpBibz4oOS1JXD29gdxM6AKAiivS/va+nlOL3IeBELmVpSRq1kS3VDuxRp4tt7gM/RfuxMiVe/Hsfw/B3uQX4cGe0Su+7MtTmLz+oOTyhpuh3O4ue5L1yWlJktNC5cvsfUiJCA60+jAUERIIvV7w+UCS3MMzrobk1wwzot7d9RNe2X7c9gv86FpoPPD29O9XsGTHjw75+GP7JWLxjpMO2FPzVVyV1iLQOlQldl/Zs/yJ3CUi5Cz7wNXTG9jbajOmX3ss2XGyyXE2qLhSh1GrOHuUnIOBEHmEAKUCMWHSLqK/V9U4uTaewdzAW2uCApW4Vi9YnSIfqg7AiN4J6N0uCpqwIGh13nNjnv9Ashi02Ls2ldx8UJZyOGka3ZC5enoDWwFqY4aAdcqAzuiiaWXzfOfsUXIGWV1j7du3h0KhaPIzefJkAEB1dTUmT56M1q1bo2XLlhg2bBjOnz9vso+zZ88iKysLISEhiImJwYwZM3Dt2jWTMl999RV69uwJtVqNTp06Yc2aNU3qsnz5crRv3x5BQUFIS0vD/v37TbZLqQt5Ft5MrrM08Naa6jq91SAoMECBqpp6rNp9GqNW7cPlGmktMZ4gK0WD8JBAkzXCXLU21eDkOOzKHoD3xvfF0hGpeG98X+zKHmCyf66e3kDOuL/GAavhOK9/Mg0RwYFmX+NP463IdWS1CB04cAD19denxBYXF2PgwIH405/+BACYNm0atm3bhs2bNyM8PBxTpkzBQw89hN27dwMA6uvrkZWVBY1Ggz179qCsrAyPP/44AgMD8eqrrwIASktLkZWVhYkTJ2L9+vXIz8/Hk08+ibi4OGRmZgIA3n//fUyfPh05OTlIS0vDkiVLkJmZiRMnTiAmJkZSXcjzeMJq755AzswbOerqTfd4uUba9HZPsO2wFtsOX1+uw7iLxFkrohuz1ZLE1dOvs9SK1jiPUONWNaDhOCoVCqvdpZxOT47WrLXGpk6diq1bt+LkyZPQ6XRo06YNNmzYgIcffhgAcPz4cXTr1g0FBQXo27cvPvvsM9x77704d+4cYmNjAQA5OTnIzs7Gb7/9BpVKhezsbGzbtg3Fxden044YMQIVFRXIzc0FAKSlpeHWW2/FsmXLAAB6vR4JCQl4+umnMXPmTFRWVtqsixRca8z1LK2r5WlrjzlTQclFjFy5193VsJsqQIHaeuc+rTvrfGhuMkTmEbpOTmZpYx8X/SopY/rSEal4IPUGJ9ScfIFL1hqrra3FunXrMH36dCgUChQWFqKurg4ZGRlima5du6Jt27Zi8FFQUICUlBQxCAKAzMxMTJo0CUeOHEGPHj1QUFBgsg9DmalTp4rvW1hYiFmzZonblUolMjIyUFBQAACS6mJOTU0Namqujz/R6XT2Hh6yk9QxGb7M2wfUGgdBCgXsntlmjTMSbDoiiHFVC5WBJ2exNteKJqUFh13k5Gp2B0JbtmxBRUUFnnjiCQCAVquFSqVCRESESbnY2FhotVqxjHEQZNhu2GatjE6nw9WrV3Hp0iXU19ebLXP8+HHJdTFnwYIFmDdvnu0PT07l6puJp/GlC7wzgiBx33BcF4kj17py1QK9vtr6xC5ycjW78witWrUKQ4YMQXx8vCPr41azZs1CZWWl+PPzzz+7u0p+y3AzeSD1BqR3dO/K864mN+O2JkyNsKAAp9apuZz552tuC5o3JkP05SzW9ibAJLKXXS1CZ86cwY4dO/Dhhx+Kv9NoNKitrUVFRYVJS8z58+eh0WjEMo1ndxlmchmXaTy76/z58wgLC0NwcDACAgIQEBBgtozxPmzVxRy1Wg212n+T9TXmyc3uvsDS8bU18FYAMC2jM9pHh4qvyzuqxcR10hMTuppeAGZndUN0K3VDQkyhIQ1CdKgaz27+Aed10qZbm9PcFrTmrE7vDvYuButNLHWRhwcHYky/9hiYZP46zmsW2cOuQGj16tWIiYlBVlaW+LtevXohMDAQ+fn5GDZsGADgxIkTOHv2LNLT0wEA6enpeOWVV3DhwgVxdldeXh7CwsKQlJQkltm+fbvJ++Xl5Yn7UKlU6NWrF/Lz8zF06FAADYOl8/PzMWXKFMl1Iet8tdndU9g6vnLHSg1OjkPOoz0xfdMPkhc7lSKohRLV1/QO2Vd0K7XZwa1z7zcf9NniqC4Sb0uG6G2Bm70MXeTLdp7C6t2lqLhah4qrdVi84yQ2Hvi5yfeA1yyyl+xZY3q9HomJiRg5ciRee+01k22TJk3C9u3bsWbNGoSFheHpp58GAOzZswdAw/T51NRUxMfHY9GiRdBqtXjsscfw5JNPmkyfT05OxuTJkzF27Fjs3LkTf/3rX7Ft2zaT6fOjR4/Gv//9b/Tp0wdLlizBpk2bcPz4cXHskK26SOGvs8YsjZfwp5lbziTn+Mp9wq3XC9hz8ne8uv0ojp2/bHcd5QYlUqx/Mg1KhcLsZ5GbPNKR56LUWXrvje/rEYGFP82qkvpd4TWLGnPqrLEdO3bg7NmzGDt2bJNtixcvhlKpxLBhw1BTU4PMzEy89dZb4vaAgABs3boVkyZNQnp6OkJDQzF69Gi8/PLLYpnExERs27YN06ZNw9KlS3HjjTfinXfeEYMgABg+fDh+++03zJkzB1qtFqmpqcjNzTUZQG2rLmSePzS7u5Pc4yt34G2AUoHbu7TBxKudJN0sDRoHPpEhgai+pndY61KoOgDPbiqCVnd9VmbjFjDjAfKnf6/Ce/vPmpQ35shZhN42ONdfZlVJ/a4M6BrLaxY1S7PyCPk6f2wR8ranY2/jquMrJxdR4ynucqe8R4QE4on09liSL2/tMltP68atYdGhakAB/H65xiljP7wpf1W9XkD/hTttBm67sgd49Y1f6jk8O6sb5m87ZrMcr1n+Rc79m6vPkwlvGy/hbVx1fOXMPGsc9Mh9NHrtoRQ8fXdnRISYXxbB4vv+77+WZmQZzxzs1zka/TpFO20WoauW63AEf5lVJfU7cKb8ikP3R/6Hi66SCX9pdncXVx3fAKUCs7OS8NQG584km5ZxEwYnx6Gg5CIqrshfu8yTBvZ6U/4qf0g8KvU70C4qxKH7I//DQIhMeNt4CW/jquObW1yGl7cebdY+bAkPboHe7SJRrxea/bTtKU/rrkqG6AjeFLjZQ+p35bH09nhnVymvWWQ3do2RCX9pdncXVxxfw3gXrc65wUXl1WsYtWof+i/cidO/VzVrX3xat48vJx6V+l1RtVDymkXNwkCImvCm8RLeyJnH11kr11tTVlmNxTtOIlQlP7u1Ag2zx/i0TuZI/a7wmkXNwVljVvjjrDFjzNLqXM44vt60cr0nzsgizyT1u8JrFhm4ZPV58n3eNF7CGznj+HrKWBspfGlgLzmX1O8Kr1lkDwZCRF7K3NOvt4y1mZbRGVMGdObTOhG5HQMhMotNzJ7N0rpK994cB6WiYZFTT6UAsPHAz5gyoLO7q0JegtcjciYGQtQEFy/0bJbWVSqrrMbKb0vdUic5PCl3EHk+Xo/I2ThrjEwYbrKNF7/UVlZj0rqDyC0uk7Sfer2AgpKL+LjoVxSUXDSbOZjkc8esMGfxpvFM5B6Ouh4RWcMWIRI5asFVPsE5z/7ScskrtDtaq6AA/FHtmEVYAeYOIuu4ADS5CluESGTrJmvcpWHQuOVn+6FzfIJzIne0ohhy/Sx86GYo0DRpnb37Y+4gssae6xGRPdgiRCK5C4Kaa/lRKiD7CY4DIaVzdSuKcWbewclxWKFUNPmbK2D6N48LD8L9t8Th7W8axiuZW82dmX7JFi4ATa7CQIhEchYEtTRg19pQIHODZNmNJo+t9Zeaq/GMs8a5fozXt8o7qsWWonMor6oVy7cKaoHn7+mG+26JR4+2kV63KCiDcs/BBaDJVRgIkUjqIoe92kXi/73+pd03YuMWJXPBlKEbjRmHmzKsvzRp3cEmLTFShKgCcKXW8jgf4yAoKjQQs7O6iX8D4yDh9O9XsHr36Sbv/0f1NTz93vco/rUCs+5J8qpFQRmUexYuAE2uwjFCJJK6yGHhmUvNGrAb0yrI5kBIoKEbjbPNmrK0rpIts7O64fDcTIy/vb2k8peq6jB5w/fILS5DbnEZ+i/ciZEr9+KZjUVYvONHq0HYv78pxfZD57xmUVDOTvI8XACaXIWBEJmQsnihvX3yxoNkORCyeQYnx2FX9gCsfzINIYHSFjv9+dIVAMDWQ1pJ5YX//WR/cMhskGDLix8Xi4GsPekUXJWCgUG55+JiquQK7BqjJozHgZjr0rCnT77xExwHQjZfgFIBpUKBK3XSprSv3XMGEcEq2QFN5dVr9lQP5VV12F9ajsqrtbK7nFzZTSUnKGcCSNezdT0iai4GQmSWtcULpQzYtTXolgMhHUNOoCgAWJJ/0nmVMWPHUS3eNTOWqKyyGhPXHUTO/57qTccfVWHxjqb1dNbYMQblno+LqZIzMRAi2awN2DU8oy0b2RORoSqLT3D+PhDSUbOTPD1Q/KjoV6tjiWZ+eBh6PTB/21GbLVXOSqLHoJzIvzEQIrsY+u7tnR4tJZjy1YGQjuz2MQSU7so2bU3rUBUuGk2tN6fiSh2e2nBQ8j6d0U3l70E5kb/jYGmym2HA7nvj+2LpiFS8N74vdmUPkHwz98eBkI6enWQ8s8aTKAA8kBrvtP07spuKs5OI/JtCEAROhbBAp9MhPDwclZWVCAsLc3d1fJa/JLGr1wvov3CnxdYbQ8vDruwBsj//9kPnMPm97+EJ32ZD61Z4sAojV+51ynu8N76vw8eMMI8Qke+Qc/9m1xi5nb8MhHTm7KR7bo7HcihkdTM50gv3dENMmNokkK3XC4gIDkTF1TqHvY8zu6k4O4nIPzEQInIRZ89OykzW4OGeN+C/B3+16/XNkRQXhn6do01+F6BUYEy/9mZngNnDFd1U/hKUE9F1HCNE5CLOnJ1kyPzsjiAIAH6vqjH7+ykDOiMiJNAh7+HLY8eIyH3YIkTkIs6anWRpzTZXshS8BSgVGHNbIhbv+FHW/gztPVMzbkL76BB2UxGR0zAQInIROSkDpA4gt7Y8RGNx4UG4/5Y4fPJDmcOm20sJ3tpHh8jer6evUk+O4y+TJchzMRAiciEp+ZfkzF6yNQDbYHZWNzzRLxEBSgWeHdQV/1dwGmfKr0B3pRZbfrB/QVHhf/u2duOS2tU3O6sbolupeTP0I5ypR56AgRCRi1mbnWSpm8vS8hJSB1ZHt1KL+29842mu+duOQalUWPxMUrsEDYEa+Qe55zqRszAQInIDc7OTbK2Cbm55CTkDsJ01lkj7v3XDIkICUXHl+lR54yd7f80iTubZc64TOYvsWWO//vorHn30UbRu3RrBwcFISUnBd999J25/4oknoFAoTH4GDx5sso/y8nKMGjUKYWFhiIiIwLhx43D58mWTMocOHcLtt9+OoKAgJCQkYNGiRU3qsnnzZnTt2hVBQUFISUnB9u3bTbYLgoA5c+YgLi4OwcHByMjIwMmTrl10kkgqOXmGDAytLZZuFQo0BCS92kVKHkskl2GfxkEQYJot2x+ziJNl9pzrRM4iq0Xo0qVL6NevH+666y589tlnaNOmDU6ePInIyEiTcoMHD8bq1avFf6vVapPto0aNQllZGfLy8lBXV4cxY8ZgwoQJ2LBhA4CGjJCDBg1CRkYGcnJycPjwYYwdOxYRERGYMGECAGDPnj0YOXIkFixYgHvvvRcbNmzA0KFDcfDgQSQnJwMAFi1ahDfffBNr165FYmIiZs+ejczMTBw9ehRBQVxAkTyLPXmGpA7ALjxzyeXrkTV+smfCQjKQc65zMDU5m6wlNmbOnIndu3fj22+/tVjmiSeeQEVFBbZs2WJ2+7Fjx5CUlIQDBw6gd+/eAIDc3Fzcc889+OWXXxAfH48VK1bghRdegFarhUqlEt97y5YtOH78OABg+PDhqKqqwtatW8V99+3bF6mpqcjJyYEgCIiPj8ezzz6Lv/3tbwCAyspKxMbGYs2aNRgxYoTNz8slNsiVCkouSlqSwtzyErYGnX508BdM2/SDw+sslTOWxCDvJfVcn5bRGRsP/MzB1CSbnPu3rK6xTz75BL1798af/vQnxMTEoEePHli5cmWTcl999RViYmLQpUsXTJo0CRcvXhS3FRQUICIiQgyCACAjIwNKpRL79u0Ty9xxxx1iEAQAmZmZOHHiBC5duiSWycjIMHnfzMxMFBQUAABKS0uh1WpNyoSHhyMtLU0s01hNTQ10Op3JD5GrSO3mMjdV3dYCuOU2VoF3NmstAPV6AQUlF/Fx0a8oKLmIer0HLJhGTiXlXI8MCcTiHScdtkAxkSWyAqGffvoJK1asQOfOnfH5559j0qRJ+Otf/4q1a9eKZQYPHoz//Oc/yM/Px8KFC/H1119jyJAhqK+vBwBotVrExMSY7LdFixaIioqCVqsVy8TGxpqUMfzbVhnj7cavM1emsQULFiA8PFz8SUhIkH5wiJqpuaugGwZgP5B6A9I7tjYpF9VSbfY1jYWoAuRWWxJLg7oNGbFHrtyLZzYWYeTKvei/cCdvcj5OyrluKRw2/H7ep0cZNJNDyAqE9Ho9evbsiVdffRU9evTAhAkTMH78eOTk5IhlRowYgfvvvx8pKSkYOnQotm7digMHDuCrr75ydN0dbtasWaisrBR/fv75Z3dXifyMswYVa8KkjYn7yx0dbZaxZ3TGJTNLcBhmsfGJ3z9ZO9enZtzUZPC9MQ6mJkeSNVg6Li4OSUlJJr/r1q0bPvjgA4uv6dChA6Kjo3Hq1Cncfffd0Gg0uHDhgkmZa9euoby8HBqNBgCg0Whw/vx5kzKGf9sqY7zd8Lu4uDiTMqmpqWbrqlarmwzsJnI1ZwwqNnRFWBswHRcehCkDOqGLpiXmfnIUWl3TcRkAmoxFighugYqr16y+//xtx5CZHCd+Bk6f9i6OHLDceF9fz7gLhWcumex766FzkvZl7wLFRMZkBUL9+vXDiRMnTH73448/ol27dhZf88svv+DixYtiMJKeno6KigoUFhaiV69eAICdO3dCr9cjLS1NLPPCCy+grq4OgYENCzbm5eWhS5cu4gy19PR05OfnY+rUqeJ75eXlIT09HQCQmJgIjUaD/Px8MfDR6XTYt28fJk2aJOdjE7mco1dBN55dBljP5WMrEGu8TS8IGPXOPqvvb3h6N3wmOdOnOcjavRyZ/dnavh5IvUH8nTMXKCZqTFbX2LRp07B37168+uqrOHXqFDZs2IC3334bkydPBgBcvnwZM2bMwN69e3H69Gnk5+fjgQceQKdOnZCZmQmgoQVp8ODBGD9+PPbv34/du3djypQpGDFiBOLj4wEAjzzyCFQqFcaNG4cjR47g/fffx9KlSzF9+nSxLs888wxyc3Pxxhtv4Pjx45g7dy6+++47TJkyBQCgUCgwdepU/P3vf8cnn3yCw4cP4/HHH0d8fDyGDh3qiGNH5FXkdLtZG2/UeNvvl82vPN+Y8dO7PakCyPUc2X1paV9l/0vIabyv5kwcIJJLVovQrbfeio8++gizZs3Cyy+/jMTERCxZsgSjRo0CAAQEBODQoUNYu3YtKioqEB8fj0GDBmH+/PkmXU7r16/HlClTcPfdd0OpVGLYsGF48803xe3h4eH44osvMHnyZPTq1QvR0dGYM2eOmEMIAG677TZs2LABL774Ip5//nl07twZW7ZsEXMIAcBzzz2HqqoqTJgwARUVFejfvz9yc3OZQ4j8ljO63ex5eucTv+dzZPellMWBZ354WNyXnAWKiZpLVh4hf8M8QkS21esF9F+40+ZaYruyB5iMEZL7GnKt5uS1sndf0zJuwjMZncV/c1FWspec+zfXGiOiZrHn6Z1P/J7LMJj5M4ndXlK6L6V2ca7eU4opAzqJf3dmIydXYCBE5KccNROoXi8gPFiFsf3a46OiX1FedX3as8bK07thzFLjJ35rryHnMtcCY4uU7kupXZwVV+qaDJB39MQBosYYCBH5IUd1OZjbT1SoCkNT4zEwSWMzuHLHEz/XrjLPMJhZ6lgJQ/ellAHLfRKjEBEciIqrlnMDGXCAPLkaAyEiH2Xphm/phmeYCSQ1caOl/VyqqsXq3aclBxiufOLnmBPzpAxmNmbcfQkAu0/+joKffgfQ8Lfs26HpTMMx/dpj8Y6TNvdtq/WIgSw5GgdLW8HB0uQJ7LnwW7rhz87qhvnbjlns+rA2SNm4HtEt1Xh2UxG0OvNT5z1xsLOlwM1Qu+Zk7vZm9XoBa3aXYv62Y5JfY5xgc+aHh5tkgY4ICcRrD6WYHM96vYBef8+zmDFayjnDQJakknP/ZiBkBQMhcjd7LvzWbvhSv+yNZwLZM3bE3H7cxTBLzZ4A0JfJ/bs+nt4OQ5Lj0CcxCnlHtZj4vwSdluQ0Ci6bE4wykCU5nLb6PBG5jj3J7GzlfpHKeJyGpXrI3Y87yclk7S/s+bsOSY4TA9u5nxyxWb7xwqiGAfJxMtfSk3JecxFWshfHCBF5IHuT2dm64UtlGKchd+yIpf24GzNZm7JnTJDxwOj9peUWu0WNmVsmxZ4B8lID2b0lF9Gvc7TET0XUgC1CRB7I3haM5t7IGy9dYG9g5WlLIDCTtSk5f1dzeZ3knGfmylpbwkXqPsyZvEHesh9EAAMhIo9kbwtGc27kzb3hWduPu3HtKlNy/q7muq3knGeOCC4l5yG6Wid7DTQiBkJEHsjeFgxbN3xrNOFBWP5IT4QHq/Bx0a8oKLmI6JZq2y80sx9PG7hqyGQNoMmx8cTAzdmknl8P97wBu7IHNPlb9kmMgibM9rnhqOBS7nnN8UIkBwMhIg9kbwuGtRu+NUNT4/83tf4oRq7ci2c2FmHkyr14dlMRIkICJe9ndlY3szdOT2AYqKuROVDXF0kNLD44+Cvyjmqb/D5AqcDc+7vbfB9HBZfG57Ut/jjwnZqH0+et4PR5cifDrB7A/FpctqYay5kWPSRZg9xird1T7r1p+jkT8jXILS6zOf3d1t81t7jMbB6hyJBALGiUR8gRcovLMPODw5IyVC8dkYoHUm9w6PuT92AeIQdhIETu1pwEcvV6Ac/9twgfHDxn830iQwJxyUqiu/D/tQqZS4ZnLTBj0OHZlu74UVK2Z2v5oOr1AvaWXLSaWdqRdp/6HaPe2WeznKfksCL34OrzRB6iuYFA46nG0aFqQAH8frkGBSUXTfbX+L16tYvErpO/23yPluoWFoMgoKFFqOJKHdaPS8N3Z8qxevdpkyfyUHUAMrtrcLVOb1Ina9mtI0PV0OqqUX65BlGhKmjCg10WJDE4u659dKikctYGVwcoFejXOdpl09b7dmiNuPAgaCurzbZWylkDjQhgIETkNI5aDsAw1Ti3uAx/++8PZvcHoMl7tVS3wOWaazb3f3vnaHxW3HQcSGMFP11E59iWGH1be7yz6ydU1dQDAC7X1OODg7/ig4O/inW6/5Y4vP1NaZMbVVllNZ7a8L3Z/btiqQQu0WDKG9MKGMYLTVp3sEnXrT8OfKfmY9eYFewaI3s5ejkARyybYcmUuzpi2ZclzdyLYyjgvKUSuERDU4alR2y1rnji2C8GtWQNu8aI3MjerND27q+50jtE47+Fv0Krc39WZQHyjo1UUv8mrdSB+L2qxm+6zLy1daVeLyA8WIXnBnd1S/cq+RYGQkQOJicrtJTBnI5aNsOcuPAg9O3YGiP7tMXiHT865T3kknNspJL6Nxm16vogXH9pXTCkFWjcuqLx0M9vrSWIQRDZg4EQkYM5el0rZ65/Zbh5tI8Ocdp72MPRn9me/RkWt/WHLjN71v9yB0vdm/70tyLHY0JFIgdz9ABUZw1UnZbRWbxpeNJgWMDx9bFnf/62qrnc9b9cjSvQk7MwECJyMEeva9WcZTMsiQsPwpQBnZ36HvZyxppf9n4+Zin2HPYuRExkCwMhIgdz9LpWUvZnbps5iv/9GN6/Xi+goOQith46hxG3JogDh5tLAeAvdyQiLlxeS4xx3RzJ3qVHDJzZPUnSSP0b7DCzJAiRNZw+bwWnz1NzOHp6r7X9AU3zCEWGBIrJEM29v7n9RVjJIC2V8Xs0Tl54qaoW87eZX/rDXXmEpGCWYvcrKLmIkSv32iwXFRqIAy8M9LiuPXItLrHhIAyEqLkcncXY2v7MbQNgtrytnDpTM25C++iQ/wUvNZi/7ZjpjKIwNUbcmoBrekAv6BEZokZ0KzU0YbY/o6GenpBZOrqlGs9uKsJ5XY3X5dHxN/V6Abe+sgPlVbU2yzJwJQZCDsJAiNzJWUtBGJLoWWoVMXfz9+VlKZqzuC251sufHsG7u0/bLMcFV4kJFYm8nDOz5tqT58gwo8gXeVseHX82MEkjKRDytFmQ5NkYCBF5GGfnSvGE/EX2cGarlLfk0fF3htl/tlozueAqycFAiMiDOHp5DnPcnb/IHq5YV8qXW718hfGSIID3LAlCno3T54k8iCtypTg6z5GzGVrIGh8XQwtZbnGZm2pG7mDoytQ0Ss2gCQ/ieC6yC1uEiDyIK7qtvGmhTVe0kJH3YVcmORJbhIg8iKu6rbzlqZrZhMkST18ShLyH7EDo119/xaOPPorWrVsjODgYKSkp+O6778TtgiBgzpw5iIuLQ3BwMDIyMnDy5EmTfZSXl2PUqFEICwtDREQExo0bh8uXL5uUOXToEG6//XYEBQUhISEBixYtalKXzZs3o2vXrggKCkJKSgq2b99usl1KXYg8iSu7rQYnx2FX9gC8N74vlo5IxXvj+2JX9gBZQZAhM/XHRb+ioOSiw9d58taB3UTkPWQFQpcuXUK/fv0QGBiIzz77DEePHsUbb7yByMhIscyiRYvw5ptvIicnB/v27UNoaCgyMzNRXX39QjVq1CgcOXIEeXl52Lp1K7755htMmDBB3K7T6TBo0CC0a9cOhYWFeP311zF37ly8/fbbYpk9e/Zg5MiRGDduHL7//nsMHToUQ4cORXFxsay6EHkSRyzPISc4ac5TdW5xGfov3ImRK/fimY1FGLlyL/ov3OnQMTveOLCbiLyLrISKM2fOxO7du/Htt9+a3S4IAuLj4/Hss8/ib3/7GwCgsrISsbGxWLNmDUaMGIFjx44hKSkJBw4cQO/evQEAubm5uOeee/DLL78gPj4eK1aswAsvvACtVguVSiW+95YtW3D8+HEAwPDhw1FVVYWtW7eK79+3b1+kpqYiJydHUl1sYUJFchd7Z0m5YnaV4X2sZaZ2VPeaIfmjtrKamZ+JSDI5929ZLUKffPIJevfujT/96U+IiYlBjx49sHLlSnF7aWkptFotMjIyxN+Fh4cjLS0NBQUFAICCggJERESIQRAAZGRkQKlUYt++fWKZO+64QwyCACAzMxMnTpzApUuXxDLG72MoY3gfKXVprKamBjqdzuTH0zm7a4Lcw55uK1fNrrI1gBloGMDsiHPR0QvYEhE1JmvW2E8//YQVK1Zg+vTpeP7553HgwAH89a9/hUqlwujRo6HVNqz6Gxsba/K62NhYcZtWq0VMTIxpJVq0QFRUlEmZxMTEJvswbIuMjIRWq7X5Prbq0tiCBQswb948aQfDA7jq6Z/cQ05eG1fOrpI6gHnvTxfRr1O0WD97Z/gw8zMROZOsQEiv16N379549dVXAQA9evRAcXExcnJyMHr0aKdU0JVmzZqF6dOni//W6XRISEhwY40sc3b2YfIu9iybYS+pA5Mnrz+I14alAECzA3ZOlyYiZ5HVNRYXF4ekpCST33Xr1g1nz54FAGg0GgDA+fPnTcqcP39e3KbRaHDhwgWT7deuXUN5eblJGXP7MH4PS2WMt9uqS2NqtRphYWEmP57IlV0T5B1cObtK6sDkiqt1mLjuICY6qLuO06WJyBlkBUL9+vXDiRMnTH73448/ol27dgCAxMREaDQa5Ofni9t1Oh327duH9PR0AEB6ejoqKipQWFgoltm5cyf0ej3S0tLEMt988w3q6urEMnl5eejSpYs4Qy09Pd3kfQxlDO8jpS7eirlVqDFXzq7q1S4SimbGIAzYichTyAqEpk2bhr179+LVV1/FqVOnsGHDBrz99tuYPHkyAEChUGDq1Kn4+9//jk8++QSHDx/G448/jvj4eAwdOhRAQwvS4MGDMX78eOzfvx+7d+/GlClTMGLECMTHxwMAHnnkEahUKowbNw5HjhzB+++/j6VLl5p0Wz3zzDPIzc3FG2+8gePHj2Pu3Ln47rvvMGXKFMl18VbMrUKNuTL/0IqvTkH6XFPLGLATkSeQNUbo1ltvxUcffYRZs2bh5ZdfRmJiIpYsWYJRo0aJZZ577jlUVVVhwoQJqKioQP/+/ZGbm4ugoOtPouvXr8eUKVNw9913Q6lUYtiwYXjzzTfF7eHh4fjiiy8wefJk9OrVC9HR0ZgzZ45JrqHbbrsNGzZswIsvvojnn38enTt3xpYtW5CcnCyrLt6IuVWoMVctm1GvF7B69+lm7aOxXSd/47gfInIbWXmE/I2n5hFibhWyxNkzCQtKLmLkyr3N3o8lnPVIRI4g5/7NRVe9kDctmkmu5ezZVc7ubuWsRyJyNQZCXoq5VcgSOfmH5JLT3do4SJeCK8p7p+bkibK0L23lVZRX1SKqpRqaMHabkvMwEPJizK1CrmYYlG2pW9aYISgHgOc/Oozyqjobr2jgyJxH5HyO7I41t6/m7pPIFgZCXs6ZT/9EjVnrljUY2689BiZpTILyq3V6THu/SNZ7cdaj53NkYldL+zIoY7cpOYms6fNERIZuWU24aTdZXHgQch7tiTn3dW+S8FATJn8GI2c9ejZHJna1tq/GmHuKHI0tQkQkm9xuWTldaoZZj47IeUTO48hlXWzty559EknFQIiI7CKnW1ZKlxrAWY/exJGJXeV2g7LblByJXWNE5BKWutSMacKDOAbESzgysavcblB2m5IjsUWIiFymcZdadKgaUAC/X67hrEcvY6u7U04Xp2FftrrH2G1KzsBAiIhcijMdfYMjE7sa78vWGDJ2m5KjsWuMiIjsYqm7054uTsO+4ix0ncax25SchGuNWeGpa40RWePILL9EUjCzNHkarjVG5KecvegqkTmO7O5k1ym5GrvGiHyEITNv4wGnhiy/ucVlbqoZEZHnYiBE5AMcmeWXiMifMBAi8gFysvwSEdF1DISIfIAjs/wSEfkTBkJEPsCRWX6JiPwJAyEiH2DIzGtpcrECDbPHmJGXiMgUAyEiH2DIzAugSTDEhUyJiCxjIETkIxyZ5ZeIyF8woSKRD2m8qCkzSxMRWcdAiMjHMDMvEZF07BojIiIiv8VAiIiIiPwWAyEiIiLyWxwjROTF6vUCB0YTETUDAyEiL5VbXIZ5nx41WWMsLjwIL92XxKnyREQSsWuMyAvlFpdh0rqDTRZa1VZWY9K6g8gtLnNTzYiIvAsDISIvU68XMO/ToxDMbDP8bt6nR1GvN1eCiIiMMRAi8jL7S8ubtAQZEwCUVVZjf2m56ypFROSlGAgReZkLf1gOguwpR0Tkz2QFQnPnzoVCoTD56dq1q7j9zjvvbLJ94sSJJvs4e/YssrKyEBISgpiYGMyYMQPXrl0zKfPVV1+hZ8+eUKvV6NSpE9asWdOkLsuXL0f79u0RFBSEtLQ07N+/32R7dXU1Jk+ejNatW6Nly5YYNmwYzp8/L+fjEnmkmFZBtgvJKEdE5M9ktwh1794dZWVl4s+uXbtMto8fP95k+6JFi8Rt9fX1yMrKQm1tLfbs2YO1a9dizZo1mDNnjlimtLQUWVlZuOuuu1BUVISpU6fiySefxOeffy6Wef/99zF9+nS89NJLOHjwIG655RZkZmbiwoULYplp06bh008/xebNm/H111/j3LlzeOihh+R+XCKP0ycxCnHhQU1WmTdQoGH2WJ/EKFdWi4jIKykEQZA8onLu3LnYsmULioqKzG6/8847kZqaiiVLlpjd/tlnn+Hee+/FuXPnEBsbCwDIyclBdnY2fvvtN6hUKmRnZ2Pbtm0oLi4WXzdixAhUVFQgNzcXAJCWloZbb70Vy5YtAwDo9XokJCTg6aefxsyZM1FZWYk2bdpgw4YNePjhhwEAx48fR7du3VBQUIC+fftK+rw6nQ7h4eGorKxEWFiYpNcQuYJh1hgAk0HThuCIq80TkT+Tc/+W3SJ08uRJxMfHo0OHDhg1ahTOnj1rsn39+vWIjo5GcnIyZs2ahStXrojbCgoKkJKSIgZBAJCZmQmdTocjR46IZTIyMkz2mZmZiYKCAgBAbW0tCgsLTcoolUpkZGSIZQoLC1FXV2dSpmvXrmjbtq1YxpyamhrodDqTHyJHqdcLKCi5iI+LfkVByUWTWV3WtpkzODkOKx7tCU24afeXJjzIYUGQuTo19zMYynx08Bes+vYnfPS9tH033v/uU79j98nfJR8vIiJLZCVUTEtLw5o1a9ClSxeUlZVh3rx5uP3221FcXIxWrVrhkUceQbt27RAfH49Dhw4hOzsbJ06cwIcffggA0Gq1JkEQAPHfWq3WahmdToerV6/i0qVLqK+vN1vm+PHj4j5UKhUiIiKalDG8jzkLFizAvHnz5BwSIkmsJT8EYFdixMHJcRiYpHFKZmlz9Y0ICQQAVFyps+szmNunlH0PTo6z+lpz70VEJJWsQGjIkCHi/998881IS0tDu3btsGnTJowbNw4TJkwQt6ekpCAuLg533303SkpK0LFjR8fV2klmzZqF6dOni//W6XRISEhwY43IFxi6sRq3WWgrqzHxf91bjRkSI9pq3QlQKpDesbUDa2u5vsZBinE9pXwGAGb3aWvfk9YdxIQ7EvH2N6UWX9v4vRgMEZEczZo+HxERgZtuugmnTp0yuz0tLQ0AxO0ajabJzC3DvzUajdUyYWFhCA4ORnR0NAICAsyWMd5HbW0tKioqLJYxR61WIywszOSHqDmkJD80x12JEa3V1xwpn2HuJ0cw9xPp+zR+vQBg5bfWgyDj92IiSSKSq1mB0OXLl1FSUoK4OPNPYIZB1Ybt6enpOHz4sMnsrry8PISFhSEpKUksk5+fb7KfvLw8pKenAwBUKhV69eplUkav1yM/P18s06tXLwQGBpqUOXHiBM6ePSuWIXIFW8kPrXFHYsTm1NccAYBWVwOtzv59So1rmEiSiOwhq2vsb3/7G+677z60a9cO586dw0svvYSAgACMHDkSJSUl2LBhA+655x60bt0ahw4dwrRp03DHHXfg5ptvBgAMGjQISUlJeOyxx7Bo0SJotVq8+OKLmDx5MtRqNQBg4sSJWLZsGZ577jmMHTsWO3fuxKZNm7Bt2zaxHtOnT8fo0aPRu3dv9OnTB0uWLEFVVRXGjBkDAAgPD8e4ceMwffp0REVFISwsDE8//TTS09MlzxgjcgRHJDV0ZWJEX0jC6AufgYhcR1Yg9Msvv2DkyJG4ePEi2rRpg/79+2Pv3r1o06YNqqursWPHDjEoSUhIwLBhw/Diiy+Krw8ICMDWrVsxadIkpKenIzQ0FKNHj8bLL78slklMTMS2bdswbdo0LF26FDfeeCPeeecdZGZmimWGDx+O3377DXPmzIFWq0Vqaipyc3NNBlAvXrwYSqUSw4YNQ01NDTIzM/HWW28151gRyeaIpIauTIzoC0kYfeEzEJHryMoj5G+YR4iaq14voP/CndBWVsseI6NAw3T4XdkDHDITTIrm1NccBYDYMDUAhd3dY0oFIAjWxyMZ3svVx4uIPJNT8wgRkXQBSoU4vbzxrVlh4f+N//3SfUkuvalbq685Uj7D3Pu7Y+79SbLrovjfz/jbE23Wx13Hi4i8HwMhIiezlPwwNkyNaRmdMa5fe0SGBppsc2RiRLks1TciJFDM92OgCQ9CzqM9kdPM5I6h6gCz+17xaE/MuifJbH3sfS8iImPsGrOCXWPkSPV6QUx+ePr3K3hv/1mT7qKoUBWGpsZjYJLGYYkRm8O4voZkjQAsJnA0Vz5AqRC726zNRtOEqfHNcwNQeOaSxeSQxvuPbqkGBOD3qhqHJpIkIt8g5/4ta7A0EdnPkPwwt7gMS3b82GTMy6WqWqzefdpjbuqWkjX2SYwSA5L9peVifS2VlzIlX6urQeGZS1aTQxr2LwZElx2bTZuI/BMDISIXspVgUYGGpIADkzQeeXO3tlSIpW4pqdPZpZST8v6WWqaIiMxhIETkQrZaR4yTAjp66YzmsrZUiLXlLaROZ7dVTsr7A/at20ZE/ouDpYlcyJGtI64kZakQS8tb9EmMQlx4kMVZXwo0BCuGMUj2vv/MDw9j0rqDTQJNQ6CUW1xmcf9E5L8YCBG5kKNaR1xNTktWY1JSCNia9i7l/Suu1NkVqBGRf2MgRORCjmgdcYfmtmRZmpIvddp7c1vIuA4ZEVnCMUJELmRoHZm07iAUMM2W7MlJAR3RkjU4OQ4DkzR2DWR2VAuZp3U5EpH7MRAicjFD60jjQb0aBwzqddaMKUNLlqXuKcPyFrZasixNsZf6/s1d+sPTuhyJyP0YCBG5QXNaRyyxZ2q7VAFKBe6/JQ7//qbUYhlntmTZakkT0JD5utLCOCGpgRoR+R+OESJyE0PryAOpNyC9Y+tmB0HOnDGVW1yGt60EQRPuSHT69HRr44xyHu2J1x5KAeA567YRkXfgEhtWcIkN8ga2lrBo7qrszt6/PfWx1JLmzFYxIvIeXGKDyI84O0mjpyWBtDbOyBldjkTk2xgIEXk5Zydp9LYkkPYOyCYi/8QxQkReztlJGr01CSQRkRQMhIi8nLOTNHprEkgiIikYCBF5OUcsYeHO/RMRuRMDISIf0NwlLNy9fyIid+H0eSs4fZ68jbMyS7tq/0REjsDp80R+ytkzpjgji4h8DbvGiIiIyG8xECIiIiK/xUCIiIiI/BYDISIiIvJbHCxNduMMIiIi8nYMhMguXOWbiIh8AbvGSLbc4jJMWnewyYrk2spqTFp3ELnFZW6qGRERkTwMhEiWer2AeZ8ehbksnIbfzfv0KOr1zNNJRESej4EQybK/tLxJS5AxAUBZZTX2l5a7rlJERER2YiBEslz4w3IQZE85IiIid5IVCM2dOxcKhcLkp2vXruL26upqTJ48Ga1bt0bLli0xbNgwnD9/3mQfZ8+eRVZWFkJCQhATE4MZM2bg2rVrJmW++uor9OzZE2q1Gp06dcKaNWua1GX58uVo3749goKCkJaWhv3795tsl1IXki+mVZDtQjLKERERuZPsFqHu3bujrKxM/Nm1a5e4bdq0afj000+xefNmfP311zh37hweeughcXt9fT2ysrJQW1uLPXv2YO3atVizZg3mzJkjliktLUVWVhbuuusuFBUVYerUqXjyySfx+eefi2Xef/99TJ8+HS+99BIOHjyIW265BZmZmbhw4YLkupB9+iRGIS48CJYmySvQMHusT2KUK6tFRERkF1mrz8+dOxdbtmxBUVFRk22VlZVo06YNNmzYgIcffhgAcPz4cXTr1g0FBQXo27cvPvvsM9x77704d+4cYmNjAQA5OTnIzs7Gb7/9BpVKhezsbGzbtg3FxcXivkeMGIGKigrk5uYCANLS0nDrrbdi2bJlAAC9Xo+EhAQ8/fTTmDlzpqS6SMHV580zzBoDYDJo2hAcrXi0J6fQExGR28i5f8tuETp58iTi4+PRoUMHjBo1CmfPngUAFBYWoq6uDhkZGWLZrl27om3btigoKAAAFBQUICUlRQyCACAzMxM6nQ5HjhwRyxjvw1DGsI/a2loUFhaalFEqlcjIyBDLSKmLOTU1NdDpdCY/1NTg5DiseLQnNOGm3V+a8CAGQURE5FVkJVRMS0vDmjVr0KVLF5SVlWHevHm4/fbbUVxcDK1WC5VKhYiICJPXxMbGQqvVAgC0Wq1JEGTYbthmrYxOp8PVq1dx6dIl1NfXmy1z/PhxcR+26mLOggULMG/ePGkHw88NTo7DwCQNM0sTEZFXkxUIDRkyRPz/m2++GWlpaWjXrh02bdqE4OBgh1fO1WbNmoXp06eL/9bpdEhISHBjjTxbgFKB9I6t3V0NIiIiuzVr+nxERARuuukmnDp1ChqNBrW1taioqDApc/78eWg0GgCARqNpMnPL8G9bZcLCwhAcHIzo6GgEBASYLWO8D1t1MUetViMsLMzkh4iIiHxXswKhy5cvo6SkBHFxcejVqxcCAwORn58vbj9x4gTOnj2L9PR0AEB6ejoOHz5sMrsrLy8PYWFhSEpKEssY78NQxrAPlUqFXr16mZTR6/XIz88Xy0ipCxEREREEGZ599lnhq6++EkpLS4Xdu3cLGRkZQnR0tHDhwgVBEARh4sSJQtu2bYWdO3cK3333nZCeni6kp6eLr7927ZqQnJwsDBo0SCgqKhJyc3OFNm3aCLNmzRLL/PTTT0JISIgwY8YM4dixY8Ly5cuFgIAAITc3VyyzceNGQa1WC2vWrBGOHj0qTJgwQYiIiBC0Wq1YxlZdpKisrBQACJWVlbJeR0RERO4j5/4tKxAaPny4EBcXJ6hUKuGGG24Qhg8fLpw6dUrcfvXqVeGpp54SIiMjhZCQEOHBBx8UysrKTPZx+vRpYciQIUJwcLAQHR0tPPvss0JdXZ1JmS+//FJITU0VVCqV0KFDB2H16tVN6vKvf/1LaNu2raBSqYQ+ffoIe/fuNdkupS62MBAiIiLyPnLu37LyCPkb5hEiIiLyPk7NI0RERETkKxgIERERkd9iIERERER+i4EQERER+S0GQkREROS3GAgRERGR32IgRERERH6LgRARERH5LQZCRERE5LcYCBEREZHfYiBEREREfouBEBEREfktBkJERETktxgIERERkd9iIERERER+i4EQERER+S0GQkREROS3GAgRERGR32IgRERERH6LgRARERH5LQZCRERE5LcYCBEREZHfYiBEREREfouBEBEREfktBkJERETktxgIERERkd9q4e4KkHvV6wXsLy3HhT+qEdMqCH0SoxCgVLi7WkRERC7BQMiP5RaXYd6nR1FWWS3+Li48CC/dl4TByXFurBkREZFrsGvMT+UWl2HSuoMmQRAAaCurMWndQeQWl7mpZkRERK7DQMgP1esFzPv0KAQz2wy/m/fpUdTrzZUgIiLyHQyE/ND+0vImLUHGBABlldXYX1ruukoRERG5AQMhP3ThD8tBkD3liIiIvFWzAqHXXnsNCoUCU6dOFX935513QqFQmPxMnDjR5HVnz55FVlYWQkJCEBMTgxkzZuDatWsmZb766iv07NkTarUanTp1wpo1a5q8//Lly9G+fXsEBQUhLS0N+/fvN9leXV2NyZMno3Xr1mjZsiWGDRuG8+fPN+cj+4SYVkEOLUdEROSt7A6EDhw4gH//+9+4+eabm2wbP348ysrKxJ9FixaJ2+rr65GVlYXa2lrs2bMHa9euxZo1azBnzhyxTGlpKbKysnDXXXehqKgIU6dOxZNPPonPP/9cLPP+++9j+vTpeOmll3Dw4EHccsstyMzMxIULF8Qy06ZNw6efforNmzfj66+/xrlz5/DQQw/Z+5F9Rp/EKMSFB8HSJHkFGmaP9UmMcmW1iIiIXM6uQOjy5csYNWoUVq5cicjIyCbbQ0JCoNFoxJ+wsDBx2xdffIGjR49i3bp1SE1NxZAhQzB//nwsX74ctbW1AICcnBwkJibijTfeQLdu3TBlyhQ8/PDDWLx4sbiff/7znxg/fjzGjBmDpKQk5OTkICQkBO+++y4AoLKyEqtWrcI///lPDBgwAL169cLq1auxZ88e7N27156P7TMClAq8dF8SADQJhgz/fum+JOYTIiIin2dXIDR58mRkZWUhIyPD7Pb169cjOjoaycnJmDVrFq5cuSJuKygoQEpKCmJjY8XfZWZmQqfT4ciRI2KZxvvOzMxEQUEBAKC2thaFhYUmZZRKJTIyMsQyhYWFqKurMynTtWtXtG3bVizTWE1NDXQ6ncmPrxqcHIcVj/aEJty0+0sTHoQVj/ZkHiEiIvILshMqbty4EQcPHsSBAwfMbn/kkUfQrl07xMfH49ChQ8jOzsaJEyfw4YcfAgC0Wq1JEARA/LdWq7VaRqfT4erVq7h06RLq6+vNljl+/Li4D5VKhYiIiCZlDO/T2IIFCzBv3jwJR8E3DE6Ow8AkDTNLExGR35IVCP3888945plnkJeXh6Ag8wNpJ0yYIP5/SkoK4uLicPfdd6OkpAQdO3ZsXm2dbNasWZg+fbr4b51Oh4SEBDfWyPkClAqkd2zt7moQERG5hayuscLCQly4cAE9e/ZEixYt0KJFC3z99dd488030aJFC9TX1zd5TVpaGgDg1KlTAACNRtNk5pbh3xqNxmqZsLAwBAcHIzo6GgEBAWbLGO+jtrYWFRUVFss0plarERYWZvJDREREvktWIHT33Xfj8OHDKCoqEn969+6NUaNGoaioCAEBAU1eU1RUBACIi2sYc5Keno7Dhw+bzO7Ky8tDWFgYkpKSxDL5+fkm+8nLy0N6ejoAQKVSoVevXiZl9Ho98vPzxTK9evVCYGCgSZkTJ07g7NmzYhkiIiLyb7K6xlq1aoXk5GST34WGhqJ169ZITk5GSUkJNmzYgHvuuQetW7fGoUOHMG3aNNxxxx3iNPtBgwYhKSkJjz32GBYtWgStVosXX3wRkydPhlqtBgBMnDgRy5Ytw3PPPYexY8di586d2LRpE7Zt2ya+7/Tp0zF69Gj07t0bffr0wZIlS1BVVYUxY8YAAMLDwzFu3DhMnz4dUVFRCAsLw9NPP4309HT07du3WQeNiIiIfINDV59XqVTYsWOHGJQkJCRg2LBhePHFF8UyAQEB2Lp1KyZNmoT09HSEhoZi9OjRePnll8UyiYmJ2LZtG6ZNm4alS5fixhtvxDvvvIPMzEyxzPDhw/Hbb79hzpw50Gq1SE1NRW5urskA6sWLF0OpVGLYsGGoqalBZmYm3nrrLUd+ZCIiIvJiCkEQuLKmBTqdDuHh4aisrOR4ISIiIi8h5/7NtcaIiIjIbzEQIiIiIr/FQIiIiIj8FgMhIiIi8lsOnTXmawzjyH15zTEiIiJfY7hvS5kPxkDIij/++AMAfH6ZDSIiIl/0xx9/IDw83GoZTp+3Qq/X49y5c2jVqhUUCtcvRGpY6+znn3/m9P3/4TFpisfEFI9HUzwmTfGYmPK14yEIAv744w/Ex8dDqbQ+CogtQlYolUrceOON7q4G1z0zg8ekKR4TUzweTfGYNMVjYsqXjoetliADDpYmIiIiv8VAiIiIiPwWAyEPplar8dJLL4mL0RKPiTk8JqZ4PJriMWmKx8SUPx8PDpYmIiIiv8UWISIiIvJbDISIiIjIbzEQIiIiIr/FQIiIiIj8FgOhZmjfvj0UCkWTn8mTJ+P06dNmtykUCmzevFnch7ntGzduNHmfr776Cj179oRarUanTp2wZs2aJnVZvnw52rdvj6CgIKSlpWH//v0m26urqzF58mS0bt0aLVu2xLBhw3D+/HmXHhMA0Gq1eOyxx6DRaBAaGoqePXvigw8+MNlHeXk5Ro0ahbCwMERERGDcuHG4fPmySZlDhw7h9ttvR1BQEBISErBo0aImddm8eTO6du2KoKAgpKSkYPv27SbbBUHAnDlzEBcXh+DgYGRkZODkyZMedzzM7eO1117zyuNh6fMYH5OSkhI8+OCDaNOmDcLCwvDnP/+5ybnqS+cI4Jhj4kvnSX19PWbPno3ExEQEBwejY8eOmD9/vsm6UVLq4UvniaOOiS+dJw4jkN0uXLgglJWViT95eXkCAOHLL78Url27ZrKtrKxMmDdvntCyZUvhjz/+EPcBQFi9erVJuatXr4rbf/rpJyEkJESYPn26cPToUeFf//qXEBAQIOTm5oplNm7cKKhUKuHdd98Vjhw5IowfP16IiIgQzp8/L5aZOHGikJCQIOTn5wvfffed0LdvX+G2225z6TERBEEYOHCgcOuttwr79u0TSkpKhPnz5wtKpVI4ePCguI/BgwcLt9xyi7B3717h22+/FTp16iSMHDlS3F5ZWSnExsYKo0aNEoqLi4X33ntPCA4OFv7973+LZXbv3i0EBAQIixYtEo4ePSq8+OKLQmBgoHD48GGxzGuvvSaEh4cLW7ZsEX744Qfh/vvvFxITE02Ovyccj3bt2gkvv/yyyX4uX77slcfD1jG5fPmy0KFDB+HBBx8UDh06JBw6dEh44IEHhFtvvVWor68X9+FL54ijjokvnSevvPKK0Lp1a2Hr1q1CaWmpsHnzZqFly5bC0qVLZdXDl84TRx0TXzpPHIWBkAM988wzQseOHQW9Xm92e2pqqjB27FiT3wEQPvroI4v7fO6554Tu3bub/G748OFCZmam+O8+ffoIkydPFv9dX18vxMfHCwsWLBAEQRAqKiqEwMBAYfPmzWKZY8eOCQCEgoICyZ/PHo2PSWhoqPCf//zHpExUVJSwcuVKQRAE4ejRowIA4cCBA+L2zz77TFAoFMKvv/4qCIIgvPXWW0JkZKRQU1MjlsnOzha6dOki/vvPf/6zkJWVZfI+aWlpwl/+8hdBEARBr9cLGo1GeP3118XtFRUVglqtFt577z1HfHSz5B4PQWi4cC1evNjiPr35eAiC6TH5/PPPBaVSKVRWVprUQ6FQCHl5eYIg+P45Igjyj4kg+NZ5kpWV1eRa+dBDDwmjRo2SXA9fO08ccUwEwbfOE0dh15iD1NbWYt26dRg7dqzZBVoLCwtRVFSEcePGNdk2efJkREdHo0+fPnj33XdNmjoLCgqQkZFhUj4zMxMFBQXi+xYWFpqUUSqVyMjIEMsUFhairq7OpEzXrl3Rtm1bsYwzmDsmt912G95//32Ul5dDr9dj48aNqK6uxp133il+3oiICPTu3VvcT0ZGBpRKJfbt2yeWueOOO6BSqcQymZmZOHHiBC5duiSWsXbcSktLodVqTcqEh4cjLS3NacfEnuNh8Nprr6F169bo0aMHXn/9dVy7dk3c5q3HA2h6TGpqaqBQKEySugUFBUGpVGLXrl3iZ/HVcwSw75gY+Mp5cttttyE/Px8//vgjAOCHH37Arl27MGTIEMn18LXzxBHHxMBXzhNH4aKrDrJlyxZUVFTgiSeeMLt91apV6NatG2677TaT37/88ssYMGAAQkJC8MUXX+Cpp57C5cuX8de//hVAwxiS2NhYk9fExsZCp9Ph6tWruHTpEurr682WOX78uLgPlUqFiIiIJmW0Wm0zPrV15o7Jpk2bMHz4cLRu3RotWrRASEgIPvroI3Tq1Emsa0xMjMl+WrRogaioKLGuWq0WiYmJTT6LYVtkZKTF42a8D+PXmSvjaPYcDwD461//ip49eyIqKgp79uzBrFmzUFZWhn/+85/iZ/HG4wE0PSZ9+/ZFaGgosrOz8eqrr0IQBMycORP19fUoKysT6+qr5whg3zEBfOs8mTlzJnQ6Hbp27YqAgADU19fjlVdewahRoyTXw9fOE0ccE8C3zhNHYSDkIKtWrcKQIUMQHx/fZNvVq1exYcMGzJ49u8k249/16NEDVVVVeP3118VAyJuZOyazZ89GRUUFduzYgejoaGzZsgV//vOf8e233yIlJcWNtXU+e4/H9OnTxfI333wzVCoV/vKXv2DBggVenw6/8TFp06YNNm/ejEmTJuHNN9+EUqnEyJEj0bNnTyiV/tGAbe8x8aXzZNOmTVi/fj02bNiA7t27o6ioCFOnTkV8fDxGjx7t7uq5haOOiS+dJ47CQMgBzpw5gx07duDDDz80u/2///0vrly5gscff9zmvtLS0jB//nzU1NRArVZDo9E0mR1y/vx5hIWFITg4GAEBAQgICDBbRqPRAAA0Gg1qa2tRUVFh0ipkXMbRzB2TkpISLFu2DMXFxejevTsA4JZbbsG3336L5cuXIycnBxqNBhcuXDDZ17Vr11BeXm7yecx9XsM2a2WMtxt+FxcXZ1ImNTW1uR+/CXuPhzlpaWm4du0aTp8+jS5dunjl8QAsf28GDRqEkpIS/P7772jRogUiIiKg0WjQoUMHsa6+eI4A9h8Tc7z5PJkxYwZmzpyJESNGAABSUlJw5swZLFiwAKNHj5ZUD187TxxxTMzx5vPEUfzjEcvJVq9ejZiYGGRlZZndvmrVKtx///1o06aNzX0VFRUhMjJSjMzT09ORn59vUiYvLw/p6ekAAJVKhV69epmU0ev1yM/PF8v06tULgYGBJmVOnDiBs2fPimUczdwxuXLlCgA0ebIPCAiAXq8H0PB5KyoqUFhYKG7fuXMn9Ho90tLSxDLffPMN6urqxDJ5eXno0qULIiMjxTLWjltiYiI0Go1JGZ1Oh3379jnlmNh7PMwpKiqCUqkUm/298XgAtr830dHRiIiIwM6dO3HhwgXcf//94mfxxXMEsP+YmOPN58mVK1esfi+k1MPXzhNHHBNzvPk8cRg3D9b2evX19ULbtm2F7Oxss9tPnjwpKBQK4bPPPmuy7ZNPPhFWrlwpHD58WDh58qTw1ltvCSEhIcKcOXPEMobp8zNmzBCOHTsmLF++3Oz0ebVaLaxZs0Y4evSoMGHCBCEiIkLQarVimYkTJwpt27YVdu7cKXz33XdCenq6kJ6e7sAjcZ2lY1JbWyt06tRJuP3224V9+/YJp06dEv7xj38ICoVC2LZtm1hu8ODBQo8ePYR9+/YJu3btEjp37mwy5bWiokKIjY0VHnvsMaG4uFjYuHGjEBIS0mR6Z4sWLYR//OMfwrFjx4SXXnrJ7PTOiIgI4eOPPxanJDtjemdzjseePXuExYsXC0VFRUJJSYmwbt06oU2bNsLjjz/utcfD2jERBEF49913hYKCAuHUqVPC//3f/wlRUVHC9OnTTcr42jnS3GPia+fJ6NGjhRtuuEGcKv7hhx8K0dHRwnPPPSerHr50njjimPjaeeIoDISa6fPPPxcACCdOnDC7fdasWUJCQoJJvg+Dzz77TEhNTRVatmwphIaGCrfccouQk5PTpOyXX34ppKamCiqVSujQoYOwevXqJvv617/+JbRt21ZQqVRCnz59hL1795psv3r1qvDUU08JkZGRQkhIiPDggw8KZWVl9n9wK6wdkx9//FF46KGHhJiYGCEkJES4+eabm0wfv3jxojBy5EihZcuWQlhYmDBmzBiT3EuCIAg//PCD0L9/f0GtVgs33HCD8NprrzV5r02bNgk33XSToFKphO7du5sEW4LQMMVz9uzZQmxsrKBWq4W7777b4t+xOZpzPAoLC4W0tDQhPDxcCAoKErp16ya8+uqrQnV1tcl+vOl4CIL1Y5KdnS3ExsYKgYGBQufOnYU33nijSUoKXztHBKF5x8TXzhOdTic888wzQtu2bYWgoCChQ4cOwgsvvGAypVtKPXzpPHHEMfG188RRFIJgNFebiIiIyI9wjBARERH5LQZCRERE5LcYCBEREZHfYiBEREREfouBEBEREfktBkJERETktxgIERERkd9iIERERER+i4EQERER+S0GQkREROS3GAgRERGR32IgRERERH7r/wMqrrSvLSCZ1QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and compute current unit usage with these whole-unit, possibly discontig HDs\n"
     ]
    },
    {
     "data": {
      "image/png": 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7DR8+XC+++KJ27typl156SaFQSBMnTtSnn37a4hiv1yu32x3ePB5Pe8sEAADXOIdlWVZ7Bv70pz/V66+/rgMHDqh///5tHnfhwgWNHDlSs2fP1lNPPXXFPsFgUMFgMPw6EAjI4/HI7/fL5XK1p1ygXbhM0zU+WZ5tugQAERAIBOR2u696/rZ1z8gl8+fP165du1RRUWEriEjSDTfcoLS0NB0/frzFPk6nU06nsz2lAQCAKGPrMo1lWZo/f7527NihN998U4MHD7a9w6amJh05ckRJSUm2xwIAgO7H1spIfn6+iouLtXPnTvXu3Vs+n0+S5Ha71atXL0lSbm6ukpOT5fV6JUnLli3ThAkTNHToUJ09e1YrVqzQiRMnNHfu3E4+FAAAEI1shZF169ZJkiZPntysffPmzfqbv/kbSVJtba1iYr5ZcPnyyy/1yCOPyOfz6ZZbblF6eroOHjyoUaNGdaxyAADQLbT7Btau1NYbYIDOxg2sXYMbWIHuqa3nb55NAwAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAoW2HE6/XqnnvuUe/evRUfH6+cnBwdO3bsquO2bdumESNGKC4uTmPGjNGePXvaXTAAAOhebIWRt956S/n5+XrnnXe0f/9+XbhwQd/73vfU2NjY4piDBw9q9uzZevjhh3X48GHl5OQoJydHR48e7XDxAAAg+jksy7LaO/jMmTOKj4/XW2+9pXvvvfeKfWbNmqXGxkbt2rUr3DZhwgSlpqZq/fr1bdpPIBCQ2+2W3++Xy+Vqb7mAbYMW7TZdwnXhk+XZpksAEAFtPX/37MhO/H6/JKlPnz4t9qmsrFRBQUGztqysLJWWlrY4JhgMKhgMhl8HAoGOlIlrACd1AEBL2n0DaygU0sKFCzVp0iSNHj26xX4+n08JCQnN2hISEuTz+Voc4/V65Xa7w5vH42lvmQAA4BrX7jCSn5+vo0ePqqSkpDPrkSQVFhbK7/eHt5MnT3b6PgAAwLWhXZdp5s+fr127dqmiokL9+/dvtW9iYqLq6+ubtdXX1ysxMbHFMU6nU06nsz2lAQCAKGNrZcSyLM2fP187duzQm2++qcGDB191TEZGhsrKypq17d+/XxkZGfYqBQAA3ZKtlZH8/HwVFxdr586d6t27d/i+D7fbrV69ekmScnNzlZycLK/XK0lasGCBMjMztXLlSmVnZ6ukpERVVVXauHFjJx8KgGgVjTc48w0goPPYWhlZt26d/H6/Jk+erKSkpPC2devWcJ/a2lrV1dWFX0+cOFHFxcXauHGjUlJS9Oqrr6q0tLTVm14BAMD1w9bKSFt+kqS8vPyytvvvv1/333+/nV0BAIDrBM+mAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhlO4xUVFRoxowZ6tevnxwOh0pLS1vtX15eLofDcdnm8/naWzMAAOhGbIeRxsZGpaSkaO3atbbGHTt2THV1deEtPj7e7q4BAEA31NPugOnTp2v69Om2dxQfH69vfetbtscBAIDurcvuGUlNTVVSUpKmTZumt99+u9W+wWBQgUCg2QYAALqniIeRpKQkrV+/Xq+99ppee+01eTweTZ48WYcOHWpxjNfrldvtDm8ejyfSZQIAAEMclmVZ7R7scGjHjh3KycmxNS4zM1MDBgzQb3/72yu+HwwGFQwGw68DgYA8Ho/8fr9cLld7y4VBgxbtNl0C0Kk+WZ5tugTgmhcIBOR2u696/rZ9z0hnGDdunA4cONDi+06nU06nswsrAgAAphj5nZGamholJSWZ2DUAALjG2F4ZaWho0PHjx8OvP/74Y9XU1KhPnz4aMGCACgsLderUKW3ZskWStGrVKg0ePFh33nmnvvrqK73wwgt68803tW/fvs47CgAAELVsh5GqqipNmTIl/LqgoECSlJeXp6KiItXV1am2tjb8/vnz5/UP//APOnXqlG688UaNHTtW//Ef/9HsMwAAwPWrQzewdpW23gCDaxc3sKK74QZW4Oraev7m2TQAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjLIdRioqKjRjxgz169dPDodDpaWlVx1TXl6uu+66S06nU0OHDlVRUVE7SgUAAN2R7TDS2NiolJQUrV27tk39P/74Y2VnZ2vKlCmqqanRwoULNXfuXO3du9d2sQAAoPvpaXfA9OnTNX369Db3X79+vQYPHqyVK1dKkkaOHKkDBw7o2WefVVZWlt3dAwCAbibi94xUVlZq6tSpzdqysrJUWVnZ4phgMKhAINBsAwAA3VPEw4jP51NCQkKztoSEBAUCAf3pT3+64hiv1yu32x3ePB5PpMsEAACGXJPfpiksLJTf7w9vJ0+eNF0SAACIENv3jNiVmJio+vr6Zm319fVyuVzq1avXFcc4nU45nc5IlwYAAK4BEV8ZycjIUFlZWbO2/fv3KyMjI9K7BgAAUcB2GGloaFBNTY1qamokXfzqbk1NjWprayVdvMSSm5sb7j9v3jx99NFHevTRR/Xhhx/q+eef1yuvvKKf//znnXMEAAAgqtkOI1VVVUpLS1NaWpokqaCgQGlpaXriiSckSXV1deFgIkmDBw/W7t27tX//fqWkpGjlypV64YUX+FovAACQJDksy7JMF3E1gUBAbrdbfr9fLpfLdDloh0GLdpsuAehUnyzPNl0CcM1r6/n7mvw2DQAAuH4QRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGAUYQQAABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUT1NFwD7Bi3abboEAAA6DSsjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAo9oVRtauXatBgwYpLi5O48eP17vvvtti36KiIjkcjmZbXFxcuwsGAADdi+0wsnXrVhUUFGjJkiU6dOiQUlJSlJWVpdOnT7c4xuVyqa6uLrydOHGiQ0UDAIDuw3YYeeaZZ/TII49ozpw5GjVqlNavX68bb7xRL774YotjHA6HEhMTw1tCQkKHigYAAN2HrTBy/vx5VVdXa+rUqd98QEyMpk6dqsrKyhbHNTQ0aODAgfJ4PJo5c6bef//9VvcTDAYVCASabQAAoHuyFUY+//xzNTU1XbaykZCQIJ/Pd8Uxw4cP14svvqidO3fqpZdeUigU0sSJE/Xpp5+2uB+v1yu32x3ePB6PnTIBAEAUifi3aTIyMpSbm6vU1FRlZmZq+/btuu2227Rhw4YWxxQWFsrv94e3kydPRrpMAABgSE87nW+99Vb16NFD9fX1zdrr6+uVmJjYps+44YYblJaWpuPHj7fYx+l0yul02ikNAABEKVsrI7GxsUpPT1dZWVm4LRQKqaysTBkZGW36jKamJh05ckRJSUn2KgUAAN2SrZURSSooKFBeXp7uvvtujRs3TqtWrVJjY6PmzJkjScrNzVVycrK8Xq8kadmyZZowYYKGDh2qs2fPasWKFTpx4oTmzp3buUcCAACiku0wMmvWLJ05c0ZPPPGEfD6fUlNT9cYbb4Rvaq2trVVMzDcLLl9++aUeeeQR+Xw+3XLLLUpPT9fBgwc1atSozjsKAAAQtRyWZVmmi7iaQCAgt9stv98vl8tluhzjBi3abboE4Lr3yfJs0yUA17y2nr95Ng0AADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjCCMAAMAowggAADCKMAIAAIzqaboAAIhGgxbtNl2CbZ8szzZdAnBFrIwAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwijACAACMIowAAACjeGovAFwneNIwrlWsjAAAAKMIIwAAwKh2XaZZu3atVqxYIZ/Pp5SUFD333HMaN25ci/23bdumxx9/XJ988omGDRumX//61/rBD37Q7qIBALhWcTnMPtthZOvWrSooKND69es1fvx4rVq1SllZWTp27Jji4+Mv63/w4EHNnj1bXq9Xf/EXf6Hi4mLl5OTo0KFDGj16dKccBACge4rGEzvsc1iWZdkZMH78eN1zzz1as2aNJCkUCsnj8ehnP/uZFi1adFn/WbNmqbGxUbt27Qq3TZgwQampqVq/fn2b9hkIBOR2u+X3++VyueyU2y3xxwkA6EyRWhlp6/nb1srI+fPnVV1drcLCwnBbTEyMpk6dqsrKyiuOqaysVEFBQbO2rKwslZaWtrifYDCoYDAYfu33+yVdPKjONnrJ3k7/TAAAokkkzq9//rlXW/ewFUY+//xzNTU1KSEhoVl7QkKCPvzwwyuO8fl8V+zv8/la3I/X69XSpUsva/d4PHbKBQAAbeBeFdnPP3funNxud4vvX5O/M1JYWNhsNSUUCumLL75Q37595XA4rjo+EAjI4/Ho5MmT1+1lHebgIubhIubhIubhIuaBObgk0vNgWZbOnTunfv36tdrPVhi59dZb1aNHD9XX1zdrr6+vV2Ji4hXHJCYm2uovSU6nU06ns1nbt771LTulSpJcLtd1/Y9MYg4uYR4uYh4uYh4uYh6Yg0siOQ+trYhcYut3RmJjY5Wenq6ysrJwWygUUllZmTIyMq44JiMjo1l/Sdq/f3+L/QEAwPXF9mWagoIC5eXl6e6779a4ceO0atUqNTY2as6cOZKk3NxcJScny+v1SpIWLFigzMxMrVy5UtnZ2SopKVFVVZU2btzYuUcCAACiku0wMmvWLJ05c0ZPPPGEfD6fUlNT9cYbb4RvUq2trVVMzDcLLhMnTlRxcbEWL16sxx57TMOGDVNpaWlEf2PE6XRqyZIll13quZ4wBxcxDxcxDxcxDxcxD8zBJdfKPNj+nREAAIDOxLNpAACAUYQRAABgFGEEAAAYRRgBAABGRW0YWbt2rQYNGqS4uDiNHz9e7777bot9J0+eLIfDcdmWnW32kckdZWcOJGnVqlUaPny4evXqJY/Ho5///Of66quvuqjayLEzDxcuXNCyZcs0ZMgQxcXFKSUlRW+88UYXVhsZFRUVmjFjhvr16yeHw9Hqs58uKS8v11133SWn06mhQ4eqqKgo4nVGkt05qKur0wMPPKA77rhDMTExWrhwYZfUGWl252H79u2aNm2abrvtNrlcLmVkZGjv3uh/ZpfdeThw4IAmTZqkvn37qlevXhoxYoSeffbZrik2gtrzf8Mlb7/9tnr27KnU1NSI1XdJVIaRrVu3qqCgQEuWLNGhQ4eUkpKirKwsnT59+or9t2/frrq6uvB29OhR9ejRQ/fff38XV9557M5BcXGxFi1apCVLluiDDz7Qpk2btHXrVj322GNdXHnnsjsPixcv1oYNG/Tcc8/pD3/4g+bNm6cf/vCHOnz4cBdX3rkaGxuVkpKitWvXtqn/xx9/rOzsbE2ZMkU1NTVauHCh5s6dG9UnIbtzEAwGddttt2nx4sVKSUmJcHVdx+48VFRUaNq0adqzZ4+qq6s1ZcoUzZgx47r7m7jppps0f/58VVRU6IMPPtDixYu1ePHiqP9NLLvzcMnZs2eVm5ur7373uxGq7P9jRaFx48ZZ+fn54ddNTU1Wv379LK/X26bxzz77rNW7d2+roaEhUiVGnN05yM/Pt+67775mbQUFBdakSZMiWmek2Z2HpKQka82aNc3afvSjH1kPPvhgROvsSpKsHTt2tNrn0Ucfte68885mbbNmzbKysrIiWFnXacsc/LnMzExrwYIFEavHFLvzcMmoUaOspUuXdn5BhrR3Hn74wx9aDz30UOcXZIideZg1a5a1ePFia8mSJVZKSkpE67Isy4q6lZHz58+rurpaU6dODbfFxMRo6tSpqqysbNNnbNq0SX/913+tm266KVJlRlR75mDixImqrq4OX8L46KOPtGfPHv3gBz/okpojoT3zEAwGFRcX16ytV69eOnDgQERrvdZUVlY2mzdJysrKavPfELqvUCikc+fOqU+fPqZLMerw4cM6ePCgMjMzTZfS5TZv3qyPPvpIS5Ys6bJ9XpNP7W3N559/rqampvAvvl6SkJCgDz/88Krj3333XR09elSbNm2KVIkR1545eOCBB/T555/r29/+tizL0tdff6158+ZF9WWa9sxDVlaWnnnmGd17770aMmSIysrKtH37djU1NXVFydcMn893xXkLBAL605/+pF69ehmqDKY9/fTTamho0F/91V+ZLsWI/v3768yZM/r666/15JNPau7cuaZL6lJ//OMftWjRIv3ud79Tz55dFxGibmWkozZt2qQxY8Zo3LhxpkvpUuXl5frnf/5nPf/88zp06JC2b9+u3bt366mnnjJdWpdavXq1hg0bphEjRig2Nlbz58/XnDlzmj3CALheFRcXa+nSpXrllVcUHx9vuhwjfve736mqqkrr16/XqlWr9PLLL5suqcs0NTXpgQce0NKlS3XHHXd06b6jbmXk1ltvVY8ePVRfX9+svb6+XomJia2ObWxsVElJiZYtWxbJEiOuPXPw+OOP68c//nE45Y8ZM0aNjY3627/9W/3iF7+IypNxe+bhtttuU2lpqb766iv97//+r/r166dFixbp9ttv74qSrxmJiYlXnDeXy8WqyHWqpKREc+fO1bZt2y67hHc9GTx4sKSL/0fW19frySef1OzZsw1X1TXOnTunqqoqHT58WPPnz5d08bKdZVnq2bOn9u3bp/vuuy8i+466M1BsbKzS09NVVlYWbguFQiorK1NGRkarY7dt26ZgMKiHHnoo0mVGVHvm4P/+7/8uCxw9evSQJFlR+niijvxbiIuLU3Jysr7++mu99tprmjlzZqTLvaZkZGQ0mzdJ2r9//1XnDd3Tyy+/rDlz5ujll1+O+p886EyhUEjBYNB0GV3G5XLpyJEjqqmpCW/z5s3T8OHDVVNTo/Hjx0ds31G3MiJJBQUFysvL0913361x48Zp1apVamxs1Jw5cyRJubm5Sk5OltfrbTZu06ZNysnJUd++fU2U3anszsGMGTP0zDPPKC0tTePHj9fx48f1+OOPa8aMGeFQEo3szsPvf/97nTp1SqmpqTp16pSefPJJhUIhPfrooyYPo8MaGhp0/Pjx8OuPP/5YNTU16tOnjwYMGKDCwkKdOnVKW7ZskSTNmzdPa9as0aOPPqqf/OQnevPNN/XKK69o9+7dpg6hw+zOgSTV1NSEx545c0Y1NTWKjY3VqFGjurr8TmN3HoqLi5WXl6fVq1dr/Pjx8vl8ki7e2O12u40cQ2ewOw9r167VgAEDNGLECEkXv/L89NNP6+///u+N1N9Z7MxDTEyMRo8e3Wx8fHy84uLiLmvvdBH/vk6EPPfcc9aAAQOs2NhYa9y4cdY777wTfi8zM9PKy8tr1v/DDz+0JFn79u3r4kojx84cXLhwwXryySetIUOGWHFxcZbH47H+7u/+zvryyy+7vvBOZmceysvLrZEjR1pOp9Pq27ev9eMf/9g6deqUgao713/+539aki7bLh17Xl6elZmZedmY1NRUKzY21rr99tutzZs3d3ndnak9c3Cl/gMHDuzy2juT3XnIzMxstX+0sjsP//Iv/2Ldeeed1o033mi5XC4rLS3Nev75562mpiYzB9BJ2vN38ee66qu9DsuK0jV6AADQLUTdPSMAAKB7IYwAAACjCCMAAMAowggAADCKMAIAAIwijAAAAKMIIwAAwCjCCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAw6v8BmB4faZVl2hMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"lets plot our unit-snapped HD pops vs. orig pops\")\n",
    "plt.scatter([HDvtdPop[t] for t in popHDlist], [HDvPop[t] for t in popHDlist])\n",
    "plt.axhline(aDP,ls=\"--\")\n",
    "plt.show()\n",
    "print(\"and compute current unit usage with these whole-unit, possibly discontig HDs\")\n",
    "unitUse = [0.]*nUnits\n",
    "for h in popHDlist:\n",
    "    for u in HDunitList[h]:\n",
    "        unitUse[u] += nDistricts * HDwt[h]\n",
    "plt.hist(unitUse,weights=unitPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "81a441d0-3a2f-473d-ba3b-0c8b365dfb55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(unitPop,unitUse)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "0de7c528-dafc-45d4-ac1c-d2807c6cee55",
   "metadata": {},
   "source": [
    "for u in range(nUnits):\n",
    "    if unitUse[u] < 0.4:\n",
    "        plotPoly(unitGeom[u])\n",
    "        plotCenter(u,unitGeom[u])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "30de9de6-e831-4cfa-a449-2cc7bf3d330f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classify these muni HDs by contiguity\n",
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 4\n",
      "working on HD 1402 time is now 6\n",
      "working on HD 2102 time is now 8\n",
      "working on HD 2802 time is now 12\n",
      "working on HD 3503 time is now 16\n",
      "working on HD 4204 time is now 17\n",
      "working on HD 4906 time is now 19\n",
      "working on HD 5606 time is now 21\n",
      "working on HD 6306 time is now 25\n",
      "working on HD 7006 time is now 28\n",
      "working on HD 7706 time is now 33\n",
      "working on HD 8407 time is now 36\n",
      "out of 8934 total HDs, there were 8934.0 contiguous and 7807.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 1127 and discontig only= 0\n",
      "here are the histograms of the small piece and enclave list lengths, total no = 0 1127\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And here are the small-HD-piece and small-enclave pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"classify these muni HDs by contiguity\") #take from vanillaHD\n",
    "\n",
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "      len(smallPieceLists),len(enclaveLists) )\n",
    "plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120])\n",
    "plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "         cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120])\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "6421f8ab-a699-4c29-976e-8c8e39d4597a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, let's fill in all enclaves that won't put us over 1022618 district pop vs 786629 target\n",
      "working on enclave-y HD 12 . We have evaluated 0 of 1127 enclavy HDs.Time is now 0\n",
      "working on enclave-y HD 1551 . We have evaluated 200 of 1127 enclavy HDs.Time is now 1\n",
      "working on enclave-y HD 2607 . We have evaluated 400 of 1127 enclavy HDs.Time is now 3\n",
      "working on enclave-y HD 4043 . We have evaluated 600 of 1127 enclavy HDs.Time is now 5\n",
      "working on enclave-y HD 6525 . We have evaluated 800 of 1127 enclavy HDs.Time is now 7\n",
      "working on enclave-y HD 7514 . We have evaluated 1000 of 1127 enclavy HDs.Time is now 9\n",
      "Out of 1127 HDs with enclaves 1127 had enclaves.\n",
      "Of these, 1094 wouldn't be over 1022618 if all enclaves filled, while 33 were too populous\n",
      "here is enclave pop by final pop for those we filled\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#copy enclave fix here.  Will not have any muni-discontig\n",
    "### THIS IS THE \"CANFILL\" CODE  #check if sum of enclave pops small enough to add\n",
    "maxNudgeUpPop = int(0.02 * aDP)\n",
    "maxPostFixPop = int(1.3 * aDP) #wider tolerance here for munis vs. vtd-based\n",
    "print(\"Now, let's fill in all enclaves that won't put us over\",int(maxPostFixPop),\"district pop vs\",int(aDP),\"target\")\n",
    "nOrigEnclaves = [0 for t in range(nHDs)]\n",
    "totalEnclavePop = [0. for t in range(nHDs)]\n",
    "tryToFill, canFill, cantFill = list(), list(), list()\n",
    "startTime = time.time()  #takes about xx sec per HD triage\n",
    "        \n",
    "for iii, t in enumerate(eLgenerator):\n",
    "    if iii%200 == 0:\n",
    "        print(\"working on enclave-y HD\",t,\". We have evaluated\",iii,\"of\",len(eLgenerator),\"enclavy HDs.Time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs)\n",
    "    if unbroken and not noEnclave:  #\"and unbroken\" new 1/15 - discontig HDs will usually appear to have enclaves.  We'll fix these in later block\n",
    "        tryToFill.append(t)\n",
    "        enclaveLists = getEnclaveLists(HDunitList[t], unitNbrs)\n",
    "        nOrigEnclaves[t] = len(enclaveLists)\n",
    "        totalEnclavePop[t] = np.sum( [ [np.sum([unitPop[u] for u in eL])] for eL in enclaveLists ] ) \n",
    "        if HDvPop[t] + totalEnclavePop[t] <= maxPostFixPop or totalEnclavePop[t] < maxNudgeUpPop:\n",
    "            canFill.append(t)\n",
    "            for eL in enclaveLists:\n",
    "                HDunitList[t] += eL\n",
    "                HDvPop[t] += np.sum([unitPop[u] for u in eL])\n",
    "        else:\n",
    "            cantFill.append(t)\n",
    "print(\"Out of\",len(eLgenerator),\"HDs with enclaves\",len(tryToFill),\"had enclaves.\")\n",
    "print(\"Of these,\",len(canFill),\"wouldn't be over\",maxPostFixPop,\"if all enclaves filled, while\",len(cantFill),\"were too populous\")\n",
    "plt.scatter([HDvPop[t] for t in canFill],[totalEnclavePop[t] for t in canFill])\n",
    "print(\"here is enclave pop by final pop for those we filled\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "aced004d-abcf-4b9d-8249-332156a1759a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defining and displaying current unit use after above manipulations; compare to original farther above.\n",
      "current avg use and its sd are 0.99864 0.08513\n"
     ]
    },
    {
     "data": {
      "image/png": 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AEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABghHaFmvXr1ysmJkb+/v5KSEjQvn37Llv72WefKTU1VTExMfLx8VF+fn67xvzhhx+0aNEiDRw4UP369VNqaqqqq6vb0z4AADCQ26GmqKhINptNOTk5Ki8vV2xsrFJSUnT8+PE268+ePauhQ4dq5cqVioiIaPeYS5Ys0e9+9ztt3rxZu3bt0tGjR3Xfffe52z4AADCU26EmLy9PGRkZSk9P15gxY1RQUKA+ffpow4YNbdZPnjxZq1at0uzZs+Xn59euMWtra/X6668rLy9Pd9xxh+Lj47Vx40bt3r1be/bscfcUAACAgdwKNY2NjSorK1NycvKlASwWJScnq7S0tF0NXM2YZWVlampqcqkZNWqUrr322su+b0NDg+rq6lw2AABgLrdCzcmTJ9Xc3Kzw8HCX/eHh4bLb7e1q4GrGtNvtslqtCg4Ovur3zc3NVVBQkHOLjo5uV38AAKB7MPbup6ysLNXW1jq3b7/91tstAQAAD+rlTnFoaKh8fX1b3XVUXV192UXAHTFmRESEGhsbVVNT43K15krv6+fnd9k1PAAAwDxuXamxWq2Kj49XSUmJc19LS4tKSkqUmJjYrgauZsz4+Hj17t3bpebw4cOqrKxs9/sCAACzuHWlRpJsNpvS0tI0adIkTZkyRfn5+aqvr1d6erokad68eYqKilJubq6kCwuBDx486Py5qqpKFRUV6tevn4YPH35VYwYFBWn+/Pmy2WwKCQlRYGCgHn30USUmJurGG2/skIkAAADdm9uhZtasWTpx4oSWLl0qu92uuLg4FRcXOxf6VlZWymK5dAHo6NGjmjBhgvP31atXa/Xq1UpKStLOnTuvakxJevHFF2WxWJSamqqGhgalpKTolVdeae95AwAAw/g4HA6Ht5voDHV1dQoKClJtba0CAwO93Y7RqmrO6VR9o0fGHtDXqqjgAI+M7QmfVtXqFy9/rPcevUVjo4K83Y7XMR8A3OXO32+3r9QAV1JVc07Ja3bpXFOzR8YP6O2rHU8kdatgAwDoHIQadKhT9Y0619Ss/FlxGh7Wr0PHPnL8jBYXVehUfSOhBgDQCqEGHjE8rB8fLwAAOpWxX74HAAB6Fq7UAPLc4uYjx890+JgAgLYRatDjdcbi5gF9rR4ZGwBwCaEGPZ4nFzdL3e82dADorgg1wP+Pxc0A0L2xUBgAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMEK7Qs369esVExMjf39/JSQkaN++fVes37x5s0aNGiV/f3+NGzdO27Ztc3ndx8enzW3VqlXOmpiYmFavr1y5sj3tAwAAA7kdaoqKimSz2ZSTk6Py8nLFxsYqJSVFx48fb7N+9+7dmjNnjubPn68DBw5oxowZmjFjhj799FNnzbFjx1y2DRs2yMfHR6mpqS5jPf/88y51jz76qLvtAwAAQ7kdavLy8pSRkaH09HSNGTNGBQUF6tOnjzZs2NBm/dq1azVt2jQ9+eSTGj16tFasWKGJEydq3bp1zpqIiAiX7be//a1uv/12DR061GWs/v37u9T17dvX3fYBAICh3Ao1jY2NKisrU3Jy8qUBLBYlJyertLS0zWNKS0td6iUpJSXlsvXV1dV6//33NX/+/FavrVy5UgMHDtSECRO0atUqnT9//rK9NjQ0qK6uzmUDAADm6uVO8cmTJ9Xc3Kzw8HCX/eHh4Tp06FCbx9jt9jbr7XZ7m/Vvvvmm+vfvr/vuu89l/2OPPaaJEycqJCREu3fvVlZWlo4dO6a8vLw2x8nNzdXy5cuv9tQAAEA351ao6QwbNmzQ3Llz5e/v77LfZrM5fx4/frysVqt+9atfKTc3V35+fq3GycrKcjmmrq5O0dHRnmscAAB4lVuhJjQ0VL6+vqqurnbZX11drYiIiDaPiYiIuOr6jz76SIcPH1ZRUdGP9pKQkKDz58/rm2++0ciRI1u97ufn12bYAQAAZnJrTY3ValV8fLxKSkqc+1paWlRSUqLExMQ2j0lMTHSpl6Tt27e3Wf/6668rPj5esbGxP9pLRUWFLBaLwsLC3DkFAABgKLc/frLZbEpLS9OkSZM0ZcoU5efnq76+Xunp6ZKkefPmKSoqSrm5uZKkxx9/XElJSVqzZo2mT5+uTZs2af/+/Xrttddcxq2rq9PmzZu1Zs2aVu9ZWlqqvXv36vbbb1f//v1VWlqqJUuW6MEHH9SAAQPac94AAMAwboeaWbNm6cSJE1q6dKnsdrvi4uJUXFzsXAxcWVkpi+XSBaCbbrpJhYWFys7O1rPPPqsRI0Zo69atGjt2rMu4mzZtksPh0Jw5c1q9p5+fnzZt2qRly5apoaFBQ4YM0ZIlS1zWzAAAgJ6tXQuFMzMzlZmZ2eZrO3fubLVv5syZmjlz5hXHXLBggRYsWNDmaxMnTtSePXvc7hMAAPQcPPsJAAAYgVADAACMQKgBAABG6HJfvgf8mCPHz3Tp8QAA3kGoQbcxoK9VAb19tbioosPHDujtqwF9rR0+LgCg8xBq0G1EBQdoxxNJOlXf2OFjD+hrVVRwQIePCwDoPIQadCtRwQGEDwBAm1goDAAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMwDcKA+h0nniIKI+6AECoAdBpPP1Q0h1PJBFsgB6MUAOg03jqoaRHjp/R4qIKnapvJNQAPRihBkCn4qGkADyFhcIAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARujl7QZwZVU153SqvtEjYw/oa1VUcIBHxgYAoLMRarqwqppzSl6zS+eamj0yfkBvX+14IolgAwAwAqGmCztV36hzTc3KnxWn4WH9OnTsI8fPaHFRhU7VNxJqAABGINR0A8PD+mlsVJC32wAAoEtjoTAAADACoQYAABihXaFm/fr1iomJkb+/vxISErRv374r1m/evFmjRo2Sv7+/xo0bp23btrm8/tBDD8nHx8dlmzZtmkvN999/r7lz5yowMFDBwcGaP3++zpw50572AQCAgdwONUVFRbLZbMrJyVF5ebliY2OVkpKi48ePt1m/e/duzZkzR/Pnz9eBAwc0Y8YMzZgxQ59++qlL3bRp03Ts2DHn9s4777i8PnfuXH322Wfavn273nvvPX344YdasGCBu+0DAABDuR1q8vLylJGRofT0dI0ZM0YFBQXq06ePNmzY0Gb92rVrNW3aND355JMaPXq0VqxYoYkTJ2rdunUudX5+foqIiHBuAwYMcL72+eefq7i4WP/5n/+phIQE3XLLLXr55Ze1adMmHT161N1TAAAABnIr1DQ2NqqsrEzJycmXBrBYlJycrNLS0jaPKS0tdamXpJSUlFb1O3fuVFhYmEaOHKlf//rX+u6771zGCA4O1qRJk5z7kpOTZbFYtHfv3jbft6GhQXV1dS4bAAAwl1uh5uTJk2publZ4eLjL/vDwcNnt9jaPsdvtP1o/bdo0vfXWWyopKdG//du/adeuXbrzzjvV3NzsHCMsLMxljF69eikkJOSy75ubm6ugoCDnFh0d7c6pAgCAbqZLfE/N7NmznT+PGzdO48eP17Bhw7Rz505NnTq1XWNmZWXJZrM5f6+rqyPYAABgMLeu1ISGhsrX11fV1dUu+6urqxUREdHmMREREW7VS9LQoUMVGhqqI0eOOMf424XI58+f1/fff3/Zcfz8/BQYGOiyAQAAc7kVaqxWq+Lj41VSUuLc19LSopKSEiUmJrZ5TGJioku9JG3fvv2y9ZL0l7/8Rd99950GDx7sHKOmpkZlZWXOmg8++EAtLS1KSEhw5xQAAICh3L77yWaz6T/+4z/05ptv6vPPP9evf/1r1dfXKz09XZI0b948ZWVlOesff/xxFRcXa82aNTp06JCWLVum/fv3KzMzU5J05swZPfnkk9qzZ4+++eYblZSU6J577tHw4cOVkpIiSRo9erSmTZumjIwM7du3T3/84x+VmZmp2bNnKzIysiPmAQAAdHNur6mZNWuWTpw4oaVLl8putysuLk7FxcXOxcCVlZWyWC5lpZtuukmFhYXKzs7Ws88+qxEjRmjr1q0aO3asJMnX11effPKJ3nzzTdXU1CgyMlI///nPtWLFCvn5+TnHefvtt5WZmampU6fKYrEoNTVVL7300k89fwAAYIh2LRTOzMx0Xmn5Wzt37my1b+bMmZo5c2ab9QEBAfr973//o+8ZEhKiwsJCt/oEAAA9B89+AgAARiDUAAAAIxBqAACAEQg1AADACF3iG4XhPUeOn+nS4wEAcLUINT3UgL5WBfT21eKiig4fO6C3rwb0tXb4uAAAXAmhpoeKCg7QjieSdKq+scPHHtDXqqjggA4fFwCAKyHU9GBRwQGEDwCAMVgoDAAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIAReKAlAGMcOX7GI+Py5HmgeyDUAOj2BvS1KqC3rxYXVXhk/IDevtrxRBLBBujiCDUAur2o4ADteCJJp+obO3zsI8fPaHFRhU7VNxJqgC6OUAPACFHBAYQOoIdjoTAAADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABghHaFmvXr1ysmJkb+/v5KSEjQvn37rli/efNmjRo1Sv7+/ho3bpy2bdvmfK2pqUlPP/20xo0bp759+yoyMlLz5s3T0aNHXcaIiYmRj4+Py7Zy5cr2tA8AAAzkdqgpKiqSzWZTTk6OysvLFRsbq5SUFB0/frzN+t27d2vOnDmaP3++Dhw4oBkzZmjGjBn69NNPJUlnz55VeXm5nnvuOZWXl2vLli06fPiw/v7v/77VWM8//7yOHTvm3B599FF32wcAAIZyO9Tk5eUpIyND6enpGjNmjAoKCtSnTx9t2LChzfq1a9dq2rRpevLJJzV69GitWLFCEydO1Lp16yRJQUFB2r59u+6//36NHDlSN954o9atW6eysjJVVla6jNW/f39FREQ4t759+7bjlAEAgIncCjWNjY0qKytTcnLypQEsFiUnJ6u0tLTNY0pLS13qJSklJeWy9ZJUW1srHx8fBQcHu+xfuXKlBg4cqAkTJmjVqlU6f/78ZcdoaGhQXV2dywYAAMzVy53ikydPqrm5WeHh4S77w8PDdejQoTaPsdvtbdbb7fY263/44Qc9/fTTmjNnjgIDA537H3vsMU2cOFEhISHavXu3srKydOzYMeXl5bU5Tm5urpYvX+7O6QEAgG7MrVDjaU1NTbr//vvlcDj06quvurxms9mcP48fP15Wq1W/+tWvlJubKz8/v1ZjZWVluRxTV1en6OhozzUPAAC8yq1QExoaKl9fX1VXV7vsr66uVkRERJvHREREXFX9xUDz5z//WR988IHLVZq2JCQk6Pz58/rmm280cuTIVq/7+fm1GXYAAICZ3FpTY7VaFR8fr5KSEue+lpYWlZSUKDExsc1jEhMTXeolafv27S71FwPNF198oR07dmjgwIE/2ktFRYUsFovCwsLcOQUAAGAotz9+stlsSktL06RJkzRlyhTl5+ervr5e6enpkqR58+YpKipKubm5kqTHH39cSUlJWrNmjaZPn65NmzZp//79eu211yRdCDT/8A//oPLycr333ntqbm52rrcJCQmR1WpVaWmp9u7dq9tvv139+/dXaWmplixZogcffFADBgzoqLkAAADdmNuhZtasWTpx4oSWLl0qu92uuLg4FRcXOxcDV1ZWymK5dAHopptuUmFhobKzs/Xss89qxIgR2rp1q8aOHStJqqqq0v/8z/9IkuLi4lze6w9/+INuu+02+fn5adOmTVq2bJkaGho0ZMgQLVmyxGXNDAAA6Nl8HA6Hw9tNdIa6ujoFBQWptrb2R9frdBWfVtXqFy9/rPcevUVjo4K83Q7QI/HfIeBd7vz95tlPAADACIQaAABgBEINAAAwAqEGAAAYgVADAACMQKgBAABGINQAAAAjEGoAAIARCDUAAMAIhBoAAGAEQg0AADACoQYAABiBUAMAAIxAqAEAAEYg1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEXp5uwEA6A6OHD/T4WMO6GtVVHBAh48L9FSEGgC4ggF9rQro7avFRRUdPnZAb18V/DJeA/taO3xsAhN6IkINAFxBVHCAdjyRpFP1jR067nf1jVr4/8qUtmFfh457UUBvX+14Iolggx6FUAMAPyIqOMAj4cATYUm68FHZ4qIKnapvJNSgRyHUAICXeCosAT0Vdz8BAAAjcKUGAGC0qppzHvmYT2JBdldDqAEAQ3Eb+oVAk7xml841NXtkfBZkdy2EGgAwjKdvQ+9Of8RP1TfqXFOz8mfFaXhYvw4dmwXZXQ+hBgAM46nb0LvzH/HhYf00NirI223Awwg1AGAg7qxCT8TdTwAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjNCuULN+/XrFxMTI399fCQkJ2rfvyk+Z3bx5s0aNGiV/f3+NGzdO27Ztc3nd4XBo6dKlGjx4sAICApScnKwvvvjCpeb777/X3LlzFRgYqODgYM2fP19nznT8F0sBAIDuye1buouKimSz2VRQUKCEhATl5+crJSVFhw8fVlhYWKv63bt3a86cOcrNzdUvfvELFRYWasaMGSovL9fYsWMlSS+88IJeeuklvfnmmxoyZIiee+45paSk6ODBg/L395ckzZ07V8eOHdP27dvV1NSk9PR0LViwQIWFhT9xCgAA6DlMfmyE26EmLy9PGRkZSk9PlyQVFBTo/fff14YNG/TMM8+0ql+7dq2mTZumJ598UpK0YsUKbd++XevWrVNBQYEcDofy8/OVnZ2te+65R5L01ltvKTw8XFu3btXs2bP1+eefq7i4WH/60580adIkSdLLL7+su+66S6tXr1ZkZGS7JwAAgJ7C9MdGuBVqGhsbVVZWpqysLOc+i8Wi5ORklZaWtnlMaWmpbDaby76UlBRt3bpVkvT111/LbrcrOTnZ+XpQUJASEhJUWlqq2bNnq7S0VMHBwc5AI0nJycmyWCzau3ev7r333lbv29DQoIaGBufvtbW1kqS6ujp3Tvmqnaj7QSfONPx4oRu+OlGvloazOnO6TnV1Ph06NgC468zpOrU0nNUnXx3TmdOe+be0o3ny39HuOh/1Z05r5X3jNHRQ3w4f+5kt/6dv7SfV39Jx39588e+2w+H40Vq3Qs3JkyfV3Nys8PBwl/3h4eE6dOhQm8fY7fY26+12u/P1i/uuVPO3H2316tVLISEhzpq/lZubq+XLl7faHx0dfbnT67IS873dAQBcMjff2x24z5P/jnbH+fBkz56a69OnTyso6MphydjHJGRlZblcIWppadH333+vgQMHysfH+1c96urqFB0drW+//VaBgYHebsfrmI/WmBNXzEdrzElrzIkrE+bD4XDo9OnTV7XUxK1QExoaKl9fX1VXV7vsr66uVkRERJvHREREXLH+4v+trq7W4MGDXWri4uKcNcePH3cZ4/z58/r+++8v+75+fn7y8/Nz2RccHHzlE/SCwMDAbvv/aJ7AfLTGnLhiPlpjTlpjTlx19/n4sSs0F7l1S7fValV8fLxKSkqc+1paWlRSUqLExMQ2j0lMTHSpl6Tt27c764cMGaKIiAiXmrq6Ou3du9dZk5iYqJqaGpWVlTlrPvjgA7W0tCghIcGdUwAAAIZy++Mnm82mtLQ0TZo0SVOmTFF+fr7q6+udd0PNmzdPUVFRys3NlSQ9/vjjSkpK0po1azR9+nRt2rRJ+/fv12uvvSZJ8vHx0eLFi/Uv//IvGjFihPOW7sjISM2YMUOSNHr0aE2bNk0ZGRkqKChQU1OTMjMzNXv2bO58AgAAktoRambNmqUTJ05o6dKlstvtiouLU3FxsXOhb2VlpSyWSxeAbrrpJhUWFio7O1vPPvusRowYoa1btzq/o0aSnnrqKdXX12vBggWqqanRLbfcouLiYud31EjS22+/rczMTE2dOlUWi0Wpqal66aWXfsq5e5Wfn59ycnJafUTWUzEfrTEnrpiP1piT1pgTVz1tPnwcV3OPFAAAQBfHs58AAIARCDUAAMAIhBoAAGAEQg0AADACocZD1q9fr5iYGPn7+yshIUH79u27Yn1+fr5GjhypgIAARUdHa8mSJfrhhx86qdvO4c6cNDU16fnnn9ewYcPk7++v2NhYFRcXd2K3nvXhhx/q7rvvVmRkpHx8fJzPQruSnTt3auLEifLz89Pw4cP1xhtveLzPzuTunBw7dkwPPPCArr/+elksFi1evLhT+uxM7s7Jli1b9LOf/UyDBg1SYGCgEhMT9fvf/75zmu0E7s7Hxx9/rJtvvlkDBw5UQECARo0apRdffLFzmu0k7fm35KI//vGP6tWrl/OLbk1AqPGAoqIi2Ww25eTkqLy8XLGxsUpJSWn1rcgXFRYW6plnnlFOTo4+//xzvf766yoqKtKzzz7byZ17jrtzkp2drX//93/Xyy+/rIMHD2rhwoW69957deDAgU7u3DPq6+sVGxur9evXX1X9119/renTp+v2229XRUWFFi9erEceecSoP1juzklDQ4MGDRqk7OxsxcbGerg773B3Tj788EP97Gc/07Zt21RWVqbbb79dd999d4/976Zv377KzMzUhx9+qM8//1zZ2dnKzs52fk+aCdydk4tqamo0b948TZ061UOdeYkDHW7KlCmORYsWOX9vbm52REZGOnJzc9usX7RokeOOO+5w2Wez2Rw333yzR/vsTO7OyeDBgx3r1q1z2Xffffc55s6d69E+vUGS4ze/+c0Va5566inHDTfc4LJv1qxZjpSUFA925j1XMyd/LSkpyfH44497rJ+uwN05uWjMmDGO5cuXd3xDXtbe+bj33nsdDz74YMc31AW4MyezZs1yZGdnO3JychyxsbEe7aszcaWmgzU2NqqsrEzJycnOfRaLRcnJySotLW3zmJtuukllZWXOj2O++uorbdu2TXfddVen9Oxp7ZmThoYGly9flKSAgAB9/PHHHu21qyotLXWZP0lKSUm57PwB0oXH2Jw+fVohISHebqVLOHDggHbv3q2kpCRvt+JVGzdu1FdffaWcnBxvt9LhjH1Kt7ecPHlSzc3Nzm9Yvig8PFyHDh1q85gHHnhAJ0+e1C233CKHw6Hz589r4cKFxnz81J45SUlJUV5enm699VYNGzZMJSUl2rJli5qbmzuj5S7Hbre3OX91dXU6d+6cAgICvNQZurLVq1frzJkzuv/++73dilddc801OnHihM6fP69ly5bpkUce8XZLXvPFF1/omWee0UcffaRevcyLAFyp6QJ27typf/3Xf9Urr7yi8vJybdmyRe+//75WrFjh7da8Zu3atRoxYoRGjRolq9WqzMxMpaenuzyCA8DlFRYWavny5Xr33XcVFhbm7Xa86qOPPtL+/ftVUFCg/Px8vfPOO95uySuam5v1wAMPaPny5br++uu93Y5HmBfTvCw0NFS+vr6qrq522V9dXa2IiIg2j3nuuef0y1/+0vm/HsaNG+d8FtY///M/d/s/5O2Zk0GDBmnr1q364Ycf9N133ykyMlLPPPOMhg4d2hktdzkRERFtzl9gYCBXadDKpk2b9Mgjj2jz5s2tPrbsiYYMGSLpwr+t1dXVWrZsmebMmePlrjrf6dOntX//fh04cECZmZmSLnxE6XA41KtXL/3v//6v7rjjDi93+dN077+WXZDValV8fLxKSkqc+1paWlRSUqLExMQ2jzl79myr4OLr6ytJchjwaK72zMlF/v7+ioqK0vnz5/Xf//3fuueeezzdbpeUmJjoMn+StH379h+dP/Q877zzjtLT0/XOO+9o+vTp3m6ny2lpaVFDQ4O32/CKwMBA/d///Z8qKiqc28KFCzVy5EhVVFQoISHB2y3+ZFyp8QCbzaa0tDRNmjRJU6ZMUX5+vurr65Weni5JmjdvnqKiopSbmytJuvvuu5WXl6cJEyYoISFBR44c0XPPPae7777bGW66O3fnZO/evaqqqlJcXJyqqqq0bNkytbS06KmnnvLmaXSYM2fO6MiRI87fv/76a1VUVCgkJETXXnutsrKyVFVVpbfeekuStHDhQq1bt05PPfWUHn74YX3wwQd699139f7773vrFDqcu3MiSRUVFc5jT5w4oYqKClmtVo0ZM6az2/cId+eksLBQaWlpWrt2rRISEmS32yVdWGQfFBTklXPoSO7Ox/r163Xttddq1KhRki7c8r569Wo99thjXunfE9yZE4vForFjx7ocHxYWJn9//1b7uy0v331lrJdfftlx7bXXOqxWq2PKlCmOPXv2OF9LSkpypKWlOX9vampyLFu2zDFs2DCHv7+/Izo62vGP//iPjlOnTnV+4x7kzpzs3LnTMXr0aIefn59j4MCBjl/+8peOqqoqL3TtGX/4wx8cklptF+cgLS3NkZSU1OqYuLg4h9VqdQwdOtSxcePGTu/bk9ozJ23VX3fddZ3eu6e4OydJSUlXrO/u3J2Pl156yXHDDTc4+vTp4wgMDHRMmDDB8corrziam5u9cwIe0J7/bv6aabd0+zgcBny+AQAAejzW1AAAACMQagAAgBEINQAAwAiEGgAAYARCDQAAMAKhBgAAGIFQAwAAjECoAQAARiDUAAAAIxBqAACAEQg1AADACIQaAABghP8PC2op0RWksMkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"defining and displaying current unit use after above manipulations; compare to original farther above.\")\n",
    "HDweight = HDwt.copy()\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "activeUnitDistro, activeUnitWeights = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > 0.01:\n",
    "        activeUnitDistro.append(unitUse[u])\n",
    "        activeUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(activeUnitDistro, weights=activeUnitWeights, bins = 20, histtype = \"step\")\n",
    "#plt.show()\n",
    "currAvg, currSD = getWeightedAvgAndSD(activeUnitDistro, activeUnitWeights)\n",
    "print(\"current avg use and its sd are\",r5(currAvg),r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "c06c0980-fad8-4596-bf2f-ad46bd6bb074",
   "metadata": {},
   "outputs": [],
   "source": [
    "savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "80050c17-7680-4ea9-b803-dfc5bc057e5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#savedHDunitList = [HDunitList[t].copy() for t in range(nHDs) ]  #safekeeping\n",
    "HDunitList = [savedHDunitList[t].copy() for t in range(nHDs) ]   #restart\n",
    "HDvPop = [0 for t in range(nHDs)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        HDvPop[t] += unitPop[u]\n",
    "plt.axvline(aDP, ls=\"--\")\n",
    "plt.axvline(minDistrictPop, ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop, ls=\"dotted\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "827b8496-d66d-40ae-9780-a20243a54f92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are the HD centers for 33 cantFill HDs with border or big enclaves\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are the HD centers for\",len(cantFill),\"cantFill HDs with border or big enclaves\")\n",
    "for u in borderUnits:\n",
    "    plotPoly(unitGeom[u],0.1)\n",
    "for t in cantFill:\n",
    "    plotPoly(HDCP[t].buffer(0.02))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "d4eec53d-e431-4e8d-a430-31ef4d1aa8c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on avg unit-to-HDcp dist for HD 0\n",
      "working on avg unit-to-HDcp dist for HD 800\n",
      "working on avg unit-to-HDcp dist for HD 1602\n",
      "working on avg unit-to-HDcp dist for HD 2402\n",
      "working on avg unit-to-HDcp dist for HD 3203\n",
      "working on avg unit-to-HDcp dist for HD 4004\n",
      "working on avg unit-to-HDcp dist for HD 4806\n",
      "working on avg unit-to-HDcp dist for HD 5606\n",
      "working on avg unit-to-HDcp dist for HD 6406\n",
      "working on avg unit-to-HDcp dist for HD 7206\n",
      "working on avg unit-to-HDcp dist for HD 8006\n",
      "working on avg unit-to-HDcp dist for HD 8807\n",
      "all unit-toHDcp distances computed\n"
     ]
    }
   ],
   "source": [
    "barredJettisonSet = set([]) #for basic muni snap, there are no special corner units to freeze\n",
    "avgDist = [0. for t in range(nHDs)]  #about 6sec per 1000 HDs\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%800 == 0:\n",
    "        print(\"working on avg unit-to-HDcp dist for HD\",t)\n",
    "    avgDist[t] =  np.sum([unitPop[u]*unitCP[u].distance(hdCP[t]) for u in HDunitList[t] ]) / HDvPop[t]\n",
    "print(\"all unit-toHDcp distances computed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "76225203-dcf1-4845-b6a2-3b75e3749b01",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for this code, all units are munis, not clusters\n",
      "remember, there are 0 units that are district-sized\n"
     ]
    }
   ],
   "source": [
    "print(\"for this code, all units are munis, not clusters\")\n",
    "allUnits = [u for u in range(nUnits)]\n",
    "wholeSet = set()\n",
    "for u in range(nUnits):\n",
    "    if unitPop[u] > minDistrictPop and unitPop[u] <= maxDistrictPop :\n",
    "        wholeSet.add(u)\n",
    "print(\"remember, there are\",len(wholeSet),\"units that are district-sized\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "e3230001-cd19-4802-bd4d-c4590bdb0f3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "badDiscoList = cantFill.copy() #sPgenerator + eLgenerator #cantFill.copy() #sPgenerator.copy()  #prep for below\n",
    "CCBgeom = list()  #no CCBgeoms in this ipynb\n",
    "#HDpoly = HDvtdGeom.copy()  #as shortcut, did not generate these shapes for the COI conversion code\n",
    "vtdArea = [tractGeom[v].area for v in range(nVTDs) ]\n",
    "HDarea = [np.sum([vtdArea[v] for v in HDvtdList[t] ]) for t in range(nHDs) ]\n",
    "HDcircle = [HDCP[t].buffer(0.23456*HDarea[t]**0.5) for t in range(nHDs) ] #approximation\n",
    "HDdiam = [HDarea[h] for h in range(nHDs)]\n",
    "maxGap = max(maxDistrictPop - aDP, 0.9 * np.median(unitPop) ) # = 0.05 * aDP\n",
    "HDnAddedUnits = [list() for h in range(nHDs)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "71d50f26-c2ec-403f-92e1-895f214f1a2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.95\n"
     ]
    }
   ],
   "source": [
    "###### how did mdp change ?????????????/\n",
    "print(minDistrictPop/aDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "187137f1-1009-49a1-8b46-715cc49e0ba8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "since these are running fast and MUSTFREE is kludgy, go straight to MUSTSPAWN code - swelling\n",
      "And now, the major Disco's  Redraw fully using HDswell\n",
      "Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\n",
      "This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is 33\n",
      "swell-generating HD 12 our 1 th HD we must regrow out of 33 0 sec elapsed\n",
      "swell-generating HD 208 our 11 th HD we must regrow out of 33 0 sec elapsed\n",
      "swell-generating HD 610 our 21 th HD we must regrow out of 33 0 sec elapsed\n",
      "swell-generating HD 7617 our 31 th HD we must regrow out of 33 1 sec elapsed\n"
     ]
    }
   ],
   "source": [
    "print(\"since these are running fast and MUSTFREE is kludgy, go straight to MUSTSPAWN code - swelling\")\n",
    "\n",
    "#THIS IS THE MUSTSPAWN code block - stolen from vanillaHD-OHredo\n",
    "print(\"And now, the major Disco's  Redraw fully using HDswell\")\n",
    "print(\"Assuming we have run the previous canSwell block.  This block repeats the code, but starting from the home unit\")\n",
    "print(\"This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "#avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(badDiscoList):\n",
    "    if i%10 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(badDiscoList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        starterU = homeU[t]\n",
    "        #distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        #starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = list( set( getAdjoiners(currList, unitNbrs) ).difference(wholeSet) )\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    #                                            subbed HDcircle for HDpoly in below\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDcircle[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]: # ... and add its nonHD, nonwhole neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap and uu not in wholeSet:  \n",
    "                    nearHDlist.append(uu)                           #subbed HDcircle for HDpoly in below\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDcircle[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c7d49e2-454f-492d-9d7f-ed28598f59da",
   "metadata": {},
   "outputs": [],
   "source": [
    "#THIS IS THE MUSTFREE CODE - for high-pop HDs with map-border enclaves, can't fill; \n",
    "#   skipped; see another code for how this runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "597a6d42-0310-4544-b7ce-543a90d09dca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "classify updated muni HDs by contiguity\n",
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 1\n",
      "working on HD 1402 time is now 2\n",
      "working on HD 2102 time is now 3\n",
      "working on HD 2802 time is now 4\n",
      "working on HD 3503 time is now 5\n",
      "working on HD 4204 time is now 6\n",
      "working on HD 4906 time is now 7\n",
      "working on HD 5606 time is now 8\n",
      "working on HD 6306 time is now 9\n",
      "working on HD 7006 time is now 10\n",
      "working on HD 7706 time is now 11\n",
      "working on HD 8407 time is now 12\n",
      "out of 8934 total HDs, there were 8934.0 contiguous and 8934.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 0 and discontig only= 0\n"
     ]
    }
   ],
   "source": [
    "print(\"classify updated muni HDs by contiguity\") #take from vanillaHD\n",
    "\n",
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "if len(smallPieceLists) + len(enclaveLists) > 0:\n",
    "    print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "          len(smallPieceLists),len(enclaveLists) )\n",
    "    plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "    plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "    plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "d5ff711c-4bf6-437e-89fe-f30dda1a51a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let's visualize over-used and underused units, < 0.75 or > 1.25\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and here is the histogram of HD pops\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "minUnitUse, maxUnitUse = 0.75, 1.25\n",
    "print(\"let's visualize over-used and underused units, <\",minUnitUse,\"or >\",maxUnitUse)\n",
    "unitUse = [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < minUnitUse:\n",
    "        plotPoly(unitGeom[u],0.4)\n",
    "        plotCenter(\"u\",unitCP[u])\n",
    "    if unitUse[u] > maxUnitUse:\n",
    "        plotPoly(unitGeom[u], 1.5)\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c],0.1)\n",
    "plt.show()\n",
    "print(\"and here is the histogram of HD pops\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist],bins = [0, 0.3*aDP, 0.45*aDP,0.6*aDP, 0.7*aDP, 0.85*aDP, 0.9*aDP, 0.95*aDP,\n",
    "         0.98*aDP, aDP, 1.02*aDP, 1.05*aDP, 1.1*aDP, 1.15*aDP, 1.3*aDP, 1.5*aDP, 2.0*aDP])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "c2428bb3-f225-4e1f-aacb-abba4cc2cffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before rectifying pops, make another copy of the current HD lists\n"
     ]
    },
    {
     "data": {
      "image/png": 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AADA2WQosHR0d6uvrU0JCQsD6hIQEud3uITUw1DGdTqdiY2P9S3Jy8pA+HwAAmG/U3iVUWlqqzs5O/3L48OFQtwQAAIIk0kpxXFycIiIi5PF4AtZ7PJ5BL6gN1phRUVGDXhMDAADGFktHWGw2mxYsWKC6ujr/Oq/Xq7q6OmVnZw+pgWCMCQAAxhZLR1gkyeFwqKioSOnp6crMzJTL5VJ3d7eKi4slSYWFhUpKSpLT6ZR05qLa/fv3+//c2tqqlpYWTZo0STNnzjyvMQEAwPhmObAUFBSovb1da9askdvtVlpammpra/0XzR46dEjh4V8duDly5Ijmz5/vf11RUaGKigrl5OSovr7+vMYEAADjm+XAIkklJSUqKSkZ8L2zIeSslJQU+Xy+bzQmAAAY30btXUIAAGD8ILAAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADDekAJLZWWlUlJSZLfblZWVpcbGxnPWb926VbNnz5bdbte8efO0Y8eOgPdPnDihkpISXXrppYqOjtacOXNUVVU1lNYAAMAYZDmw1NTUyOFwqLy8XM3NzUpNTVVeXp7a2toGrN+9e7cWL16s+++/X3v37lV+fr7y8/O1b98+f43D4VBtba3+5V/+RR9++KFWrFihkpISvfrqq0PfMwAAMGZYDizr16/XkiVLVFxc7D8ScsEFF2jz5s0D1m/YsEE333yzVq5cqauvvlpPPvmkrr32Wm3cuNFfs3v3bhUVFenGG29USkqKfvzjHys1NfVrj9wAAIDxwVJg6e3tVVNTk3Jzc78aIDxcubm5amhoGHCbhoaGgHpJysvLC6hfuHChXn31VbW2tsrn8+mtt97Sxx9/rO9///uD9tLT06Ourq6ABQAAjE2WAktHR4f6+vqUkJAQsD4hIUFut3vAbdxu99fWP/PMM5ozZ44uvfRS2Ww23XzzzaqsrNQNN9wwaC9Op1OxsbH+JTk52cquAACAUcSIu4SeeeYZvffee3r11VfV1NSkdevWadmyZXrjjTcG3aa0tFSdnZ3+5fDhwyPYMQAAGEmRVorj4uIUEREhj8cTsN7j8SgxMXHAbRITE89Zf+rUKa1evVqvvPKKFi1aJEn61re+pZaWFlVUVPQ7nXRWVFSUoqKirLQPAABGKUtHWGw2mxYsWKC6ujr/Oq/Xq7q6OmVnZw+4TXZ2dkC9JO3cudNff/r0aZ0+fVrh4YGtREREyOv1WmkPAACMUZaOsEhnbkEuKipSenq6MjMz5XK51N3dreLiYklSYWGhkpKS5HQ6JUnLly9XTk6O1q1bp0WLFmnLli3as2ePNm3aJEmKiYlRTk6OVq5cqejoaF1++eXatWuXXnjhBa1fv34YdxWjwcG2E6NiTADAyLIcWAoKCtTe3q41a9bI7XYrLS1NtbW1/gtrDx06FHC0ZOHChaqurlZZWZlWr16tWbNmadu2bZo7d66/ZsuWLSotLdW9996rP//5z7r88sv193//91q6dOkw7CJGgykTbYqeEKEVNS1BGT96QoSmTLQFZWwAQPCF+Xw+X6ibGA5dXV2KjY1VZ2enYmJiQt3OmLWvtVO3PPOuXnvwes1Nih3WsVuPndLR7t5hHfOsKRNtSpocHZSxcUYw/24AGLvO9/vb8hEWIFiSJkcTKgAAAzLitmYAAIBzIbAAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwXmSoGwAwthxsOxGUcadMtClpcnRQxgZgPgILgGExZaJN0RMitKKmJSjjR0+I0BuP5BBagHGKwAJgWCRNjtYbj+ToaHfvsI99sO2EVtS06Gh3L4EFGKcILACGTdLkaAIFgKDgolsAAGA8AgsAADAegQUAABhvSIGlsrJSKSkpstvtysrKUmNj4znrt27dqtmzZ8tut2vevHnasWNHv5oPP/xQP/jBDxQbG6uJEycqIyNDhw4dGkp7AABgjLEcWGpqauRwOFReXq7m5malpqYqLy9PbW1tA9bv3r1bixcv1v3336+9e/cqPz9f+fn52rdvn7/mv/7rv3T99ddr9uzZqq+v1x/+8Ac99thjstvtQ98zAAAwZoT5fD6flQ2ysrKUkZGhjRs3SpK8Xq+Sk5P14IMPatWqVf3qCwoK1N3drddee82/7tvf/rbS0tJUVVUlSbr77rs1YcIE/fM///OQd6Srq0uxsbHq7OxUTEzMkMfBue1r7dQtz7yr1x68XnOTYkPdDsYJ/t4BY9f5fn9bOsLS29urpqYm5ebmfjVAeLhyc3PV0NAw4DYNDQ0B9ZKUl5fnr/d6vdq+fbuuvPJK5eXlKT4+XllZWdq2bZuV1gAAwBhmKbB0dHSor69PCQkJAesTEhLkdrsH3Mbtdp+zvq2tTSdOnNDatWt1880363e/+51uv/12/fCHP9SuXbsG7aWnp0ddXV0BCwAAGJtC/uA4r9crSbrtttv08MMPS5LS0tK0e/duVVVVKScnZ8DtnE6nnnjiiRHrEwAAhI6lIyxxcXGKiIiQx+MJWO/xeJSYmDjgNomJieesj4uLU2RkpObMmRNQc/XVV5/zLqHS0lJ1dnb6l8OHD1vZFQAAMIpYCiw2m00LFixQXV2df53X61VdXZ2ys7MH3CY7OzugXpJ27tzpr7fZbMrIyNCBAwcCaj7++GNdfvnlg/YSFRWlmJiYgAUAAIxNlk8JORwOFRUVKT09XZmZmXK5XOru7lZxcbEkqbCwUElJSXI6nZKk5cuXKycnR+vWrdOiRYu0ZcsW7dmzR5s2bfKPuXLlShUUFOiGG27QTTfdpNraWv37v/+76uvrh2cvAQDAqGY5sBQUFKi9vV1r1qyR2+1WWlqaamtr/RfWHjp0SOHhXx24Wbhwoaqrq1VWVqbVq1dr1qxZ2rZtm+bOneuvuf3221VVVSWn06mHHnpIV111lV566SVdf/31w7CLAABgtLP8HBZT8RyWkcHzMBAK/L0Dxq6gPIcFAAAgFAgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGG1JgqaysVEpKiux2u7KystTY2HjO+q1bt2r27Nmy2+2aN2+eduzYMWjt0qVLFRYWJpfLNZTW8P+1Hjulfa2dw74cbDsR6l0DAIxDkVY3qKmpkcPhUFVVlbKysuRyuZSXl6cDBw4oPj6+X/3u3bu1ePFiOZ1O3XLLLaqurlZ+fr6am5s1d+7cgNpXXnlF7733nqZNmzb0PYJaj51S7rpdOnW6LyjjR0+I0JSJtqCMDQDAQCwHlvXr12vJkiUqLi6WJFVVVWn79u3avHmzVq1a1a9+w4YNuvnmm7Vy5UpJ0pNPPqmdO3dq48aNqqqq8te1trbqwQcf1Ouvv65FixYNdX8g6Wh3r06d7pOrIE0z4ycN+/hTJtqUNDl62McFAGAwlgJLb2+vmpqaVFpa6l8XHh6u3NxcNTQ0DLhNQ0ODHA5HwLq8vDxt27bN/9rr9eq+++7TypUrdc0115xXLz09Perp6fG/7urqsrAn48PM+EmamxQb6jYAAPjGLF3D0tHRob6+PiUkJASsT0hIkNvtHnAbt9v9tfU///nPFRkZqYceeui8e3E6nYqNjfUvycnJFvYEAACMJiG/S6ipqUkbNmzQ888/r7CwsPPerrS0VJ2dnf7l8OHDQewSAACEkqXAEhcXp4iICHk8noD1Ho9HiYmJA26TmJh4zvp33nlHbW1tuuyyyxQZGanIyEh9/vnneuSRR5SSkjJoL1FRUYqJiQlYAADA2GQpsNhsNi1YsEB1dXX+dV6vV3V1dcrOzh5wm+zs7IB6Sdq5c6e//r777tMf/vAHtbS0+Jdp06Zp5cqVev31163uDwAAGIMs3yXkcDhUVFSk9PR0ZWZmyuVyqbu723/XUGFhoZKSkuR0OiVJy5cvV05OjtatW6dFixZpy5Yt2rNnjzZt2iRJmjp1qqZOnRrwGRMmTFBiYqKuuuqqb7p/AABgDLAcWAoKCtTe3q41a9bI7XYrLS1NtbW1/gtrDx06pPDwrw7cLFy4UNXV1SorK9Pq1as1a9Ysbdu2rd8zWAAAAAZjObBIUklJiUpKSgZ8r76+vt+6O++8U3feeed5j//f//3fQ2lrVGo9dkpHu3uHdUyeRgsAGGuGFFgwPIL5RFqeRgsAGEsILCEUzCfS8jRaAMBYQmAxAE+kBQDg3EL+4DgAAICvQ2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMbj0fwARo1g/BI5v7sFjA4EFgDGmzLRpugJEVpR0zLsY0dPiNAbj+QQWgDDEVgAGC9pcrTeeCRHR7t7h3Xcg20ntKKmRUe7ewksgOEILABGhaTJ0YQKYBzjolsAAGA8AgsAADAegQUAABiPwAIAAIxHYAEAAMYjsAAAAOMRWAAAgPEILAAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjEdgAQAAxhtSYKmsrFRKSorsdruysrLU2Nh4zvqtW7dq9uzZstvtmjdvnnbs2OF/7/Tp0/rJT36iefPmaeLEiZo2bZoKCwt15MiRobQGAADGIMuBpaamRg6HQ+Xl5WpublZqaqry8vLU1tY2YP3u3bu1ePFi3X///dq7d6/y8/OVn5+vffv2SZJOnjyp5uZmPfbYY2pubtbLL7+sAwcO6Ac/+ME32zMAADBmhPl8Pp+VDbKyspSRkaGNGzdKkrxer5KTk/Xggw9q1apV/eoLCgrU3d2t1157zb/u29/+ttLS0lRVVTXgZ7z//vvKzMzU559/rssuu+y8+urq6lJsbKw6OzsVExNjZZdCZl9rp2555l299uD1mpsUG+p2gHGH/weB0Dvf729LR1h6e3vV1NSk3NzcrwYID1dubq4aGhoG3KahoSGgXpLy8vIGrZekzs5OhYWFafLkyYPW9PT0qKurK2ABAABjk6XA0tHRob6+PiUkJASsT0hIkNvtHnAbt9ttqf6LL77QT37yEy1evPicScvpdCo2Nta/JCcnW9kVAAAwihh1l9Dp06d11113yefz6dlnnz1nbWlpqTo7O/3L4cOHR6hLAAAw0iKtFMfFxSkiIkIejydgvcfjUWJi4oDbJCYmnlf92bDy+eef68033/za61CioqIUFRVlpX0AADBKWTrCYrPZtGDBAtXV1fnXeb1e1dXVKTs7e8BtsrOzA+olaefOnQH1Z8PKJ598ojfeeENTp0610hYAABjjLB1hkSSHw6GioiKlp6crMzNTLpdL3d3dKi4uliQVFhYqKSlJTqdTkrR8+XLl5ORo3bp1WrRokbZs2aI9e/Zo06ZNks6Elb/8y79Uc3OzXnvtNfX19fmvb7noootks9mGa18BAMAoZTmwFBQUqL29XWvWrJHb7VZaWppqa2v9F9YeOnRI4eFfHbhZuHChqqurVVZWptWrV2vWrFnatm2b5s6dK0lqbW3Vq6++KklKS0sL+Ky33npLN9544xB3DQAAjBWWA4sklZSUqKSkZMD36uvr+6278847deeddw5Yn5KSIouPggEAAOOMUXcJAQAADITAAgAAjEdgAQAAxiOwAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjDenHDwFgLDnYdiIo406ZaFPS5OigjA2MNwQWAOPWlIk2RU+I0IqalqCMHz0hQm88kkNoAYYBgQXAuJU0OVpvPJKjo929wz72wbYTWlHToqPdvQQWYBgQWACMa0mTo4MaKIJxuolTTRiPCCwAEATBPN3EqSaMRwQWAAiCYJ1u4lQTxisCCwAESbBPNwHjCc9hAQAAxuMICwBgVGs9diood3pxcbNZCCwAMArxsLszWo+dUu66XTp1um/Yx+biZrMQWABgFOFhd4GOdvfq1Ok+uQrSNDN+0rCNy8XN5iGwAMAowsPuBjYzfpLmJsWGug0EEYEFAEYZ7j7CeMRdQgAAwHgEFgAAYDwCCwAAMB6BBQAAGI/AAgAAjDekwFJZWamUlBTZ7XZlZWWpsbHxnPVbt27V7NmzZbfbNW/ePO3YsSPgfZ/PpzVr1uiSSy5RdHS0cnNz9cknnwylNQAAMAZZvq25pqZGDodDVVVVysrKksvlUl5eng4cOKD4+Ph+9bt379bixYvldDp1yy23qLq6Wvn5+WpubtbcuXMlSf/wD/+gp59+Wr/5zW80ffp0PfbYY8rLy9P+/ftlt9u/+V4CADBOjNWfKrAcWNavX68lS5aouLhYklRVVaXt27dr8+bNWrVqVb/6DRs26Oabb9bKlSslSU8++aR27typjRs3qqqqSj6fTy6XS2VlZbrtttskSS+88IISEhK0bds23X333d9k/wAAGDfG8k8VWAosvb29ampqUmlpqX9deHi4cnNz1dDQMOA2DQ0NcjgcAevy8vK0bds2SdJnn30mt9ut3Nxc//uxsbHKyspSQ0PDoIGlp6dHPT09/tednZ2SpK6uLiu7dF7au75Q+4mery+06NP2bnl7TurE8S51dYUN+/gAYMWJ413y9pzUHz79k04cH/5/S4MhWP+Ojsa5kM7MR/eJ41r7w3macfHEYR131ct/1GF3hy4MH94nCp/93vb5fOessxRYOjo61NfXp4SEhID1CQkJ+uijjwbcxu12D1jvdrv9759dN1jNQJxOp5544ol+65OTk79+RwyT7Qp1BwDwlXtdoe7AumD9Ozoa50IKXt/B/L46fvy4YmMHD0Oj9tH8paWlAUduvF6v/vznP2vq1KkKCwvt0Yquri4lJyfr8OHDiomJCWkvpmBOAjEf/TEngZiP/piT/sbCnPh8Ph0/flzTpk07Z52lwBIXF6eIiAh5PJ6A9R6PR4mJiQNuk5iYeM76s//1eDy65JJLAmrS0tIG7SUqKkpRUVEB6yZPnny+uzIiYmJiRu1foGBhTgIxH/0xJ4GYj/6Yk/5G+5yc68jKWZZua7bZbFqwYIHq6ur867xer+rq6pSdnT3gNtnZ2QH1krRz505//fTp05WYmBhQ09XVpd///veDjgkAAMYXy6eEHA6HioqKlJ6erszMTLlcLnV3d/vvGiosLFRSUpKcTqckafny5crJydG6deu0aNEibdmyRXv27NGmTZskSWFhYVqxYoV+9rOfadasWf7bmqdNm6b8/Pzh21MAADBqWQ4sBQUFam9v15o1a+R2u5WWlqba2lr/RbOHDh1SePhXB24WLlyo6upqlZWVafXq1Zo1a5a2bdvmfwaLJD366KPq7u7Wj3/8Yx07dkzXX3+9amtrR+0zWKKiolReXt7vlNV4xpwEYj76Y04CMR/9MSf9jac5CfN93X1EAAAAIcZvCQEAAOMRWAAAgPEILAAAwHgEFgAAYDwCyxBVVlYqJSVFdrtdWVlZamxsPGe9y+XSVVddpejoaCUnJ+vhhx/WF198MULdBp+V+Th9+rR++tOf6oorrpDdbldqaqpqa2tHsNvge/vtt3Xrrbdq2rRpCgsL8/921rnU19fr2muvVVRUlGbOnKnnn38+6H2OFKvz8ac//Un33HOPrrzySoWHh2vFihUj0udIsjonL7/8sr73ve/p4osvVkxMjLKzs/X666+PTLMjxOqcvPvuu7ruuus0depURUdHa/bs2frHf/zHkWl2BAzl35Gz/vM//1ORkZHnfADraENgGYKamho5HA6Vl5erublZqampysvLU1tb24D11dXVWrVqlcrLy/Xhhx/qn/7pn1RTU6PVq1ePcOfBYXU+ysrK9Ktf/UrPPPOM9u/fr6VLl+r222/X3r17R7jz4Onu7lZqaqoqKyvPq/6zzz7TokWLdNNNN6mlpUUrVqzQAw88MGa+kKzOR09Pjy6++GKVlZUpNTU1yN2FhtU5efvtt/W9731PO3bsUFNTk2666Sbdeuut4/r/m4kTJ6qkpERvv/22PvzwQ5WVlamsrMz/nK/Rzup8nHXs2DEVFhbqu9/9bpA6CxEfLMvMzPQtW7bM/7qvr883bdo0n9PpHLB+2bJlvu985zsB6xwOh++6664Lap8jxep8XHLJJb6NGzcGrPvhD3/ou/fee4PaZ6hI8r3yyivnrHn00Ud911xzTcC6goICX15eXhA7C43zmY//LScnx7d8+fKg9WMCq3Ny1pw5c3xPPPHE8DdkgKHOye233+770Y9+NPwNhZiV+SgoKPCVlZX5ysvLfampqUHtayRxhMWi3t5eNTU1KTc3178uPDxcubm5amhoGHCbhQsXqqmpyX+a5NNPP9WOHTv0F3/xFyPSczANZT56enr6PRQwOjpa7777blB7NVlDQ0PAHEpSXl7eoHMIeL1eHT9+XBdddFGoWzHG3r17tXv3buXk5IS6lZB57rnn9Omnn6q8vDzUrQy7UftrzaHS0dGhvr4+/5N9z0pISNBHH3004Db33HOPOjo6dP3118vn8+nLL7/U0qVLx8QpoaHMR15entavX68bbrhBV1xxherq6vTyyy+rr69vJFo2ktvtHnAOu7q6dOrUKUVHR4eoM5iqoqJCJ06c0F133RXqVkLu0ksvVXt7u7788ks9/vjjeuCBB0LdUkh88sknWrVqld555x1FRo69r3eOsIyA+vp6PfXUU/rlL3+p5uZmvfzyy9q+fbuefPLJULcWEhs2bNCsWbM0e/Zs2Ww2lZSUqLi4OOAnHQAMrrq6Wk888YR++9vfKj4+PtTthNw777yjPXv2qKqqSi6XSy+++GKoWxpxfX19uueee/TEE0/oyiuvDHU7QTH2IliQxcXFKSIiQh6PJ2C9x+NRYmLigNs89thjuu+++/ypf968ef7fTvq7v/u7Uf1FPZT5uPjii7Vt2zZ98cUX+p//+R9NmzZNq1at0owZM0aiZSMlJiYOOIcxMTEcXUGALVu26IEHHtDWrVv7nUYcr6ZPny7pzL+tHo9Hjz/+uBYvXhzirkbW8ePHtWfPHu3du1clJSWSzpw29Pl8ioyM1O9+9zt95zvfCXGX38zo/aYMEZvNpgULFqiurs6/zuv1qq6uTtnZ2QNuc/LkyX6hJCIiQpLkG+U/5TSU+TjLbrcrKSlJX375pV566SXddtttwW7XWNnZ2QFzKEk7d+782jnE+PLiiy+quLhYL774ohYtWhTqdozk9XrV09MT6jZGXExMjP74xz+qpaXFvyxdulRXXXWVWlpalJWVFeoWvzGOsAyBw+FQUVGR0tPTlZmZKZfLpe7ubhUXF0uSCgsLlZSUJKfTKUm69dZbtX79es2fP19ZWVk6ePCgHnvsMd16663+4DKaWZ2P3//+92ptbVVaWppaW1v1+OOPy+v16tFHHw3lbgyrEydO6ODBg/7Xn332mVpaWnTRRRfpsssuU2lpqVpbW/XCCy9IkpYuXaqNGzfq0Ucf1V//9V/rzTff1G9/+1tt3749VLswrKzOhyS1tLT4t21vb1dLS4tsNpvmzJkz0u0HhdU5qa6uVlFRkTZs2KCsrCy53W5JZy5Yj42NDck+DDerc1JZWanLLrtMs2fPlnTm1u+Kigo99NBDIel/uFmZj/DwcM2dOzdg+/j4eNnt9n7rR60Q36U0aj3zzDO+yy67zGez2XyZmZm+9957z/9eTk6Or6ioyP/69OnTvscff9x3xRVX+Ox2uy85Odn3t3/7t76jR4+OfONBYmU+6uvrfVdffbUvKirKN3XqVN99993na21tDUHXwfPWW2/5JPVbzs5DUVGRLycnp982aWlpPpvN5psxY4bvueeeG/G+g2Uo8zFQ/eWXXz7ivQeL1TnJyck5Z/1YYHVOnn76ad8111zju+CCC3wxMTG++fPn+375y1/6+vr6QrMDw2wo/9/8b2PttuYwn2+Un5MAAABjHtewAAAA4xFYAACA8QgsAADAeAQWAABgPAILAAAwHoEFAAAYj8ACAACMR2ABAADGI7AAAADjEVgAAIDxCCwAAMB4BBYAAGC8/wdEoULrsUbFBAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pre-patch avg use and its sd are 0.99864 0.08276\n"
     ]
    }
   ],
   "source": [
    "print(\"before rectifying pops, make another copy of the current HD lists\")\n",
    "latestHDlist = [HDunitList[t].copy() for t in range(nHDs)]   #More safekeeping\n",
    "latestHDpop, latestUnitUse = [0. for t in range(nHDs)], [0. for u in range(nUnits)]\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        latestHDpop[t] += unitPop[u]\n",
    "        latestUnitUse[u] += nDistricts * HDweight[t]\n",
    "latestUnitWeights, latestUnitDistro = list(), list()\n",
    "for u in range(nUnits):\n",
    "    if latestUnitUse[u] > 0.1:\n",
    "        latestUnitDistro.append(latestUnitUse[u])\n",
    "        latestUnitWeights.append(unitPop[u]/statePop)\n",
    "plt.hist(latestUnitDistro, weights=latestUnitWeights, bins = 20, histtype = \"step\")\n",
    "plt.show()\n",
    "latestAvg, latestSD = getWeightedAvgAndSD(latestUnitDistro, latestUnitWeights)\n",
    "print(\"pre-patch avg use and its sd are\",r5(latestAvg),r5(latestSD) )"
   ]
  },
  {
   "cell_type": "raw",
   "id": "878bf68e-dbd9-4d98-9bae-efbdd132eb75",
   "metadata": {},
   "source": [
    "HDunitList = [latestHDlist[t].copy() for t in range(nHDs)] #restart\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts*HDweight[t]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "4fc22ecf-9460-43ef-a595-8a39026a9c39",
   "metadata": {},
   "outputs": [],
   "source": [
    "minDistrictPop, maxDistrictPop = 0.995 * aDP, 1.005 * aDP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "6b3dd3ff-706e-4e0d-a705-3a82cd62b519",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We will now square up the 1181 HDs with pop < 782696 to within 3933\n",
      "working on squaring up HD 7 time is now 0\n",
      "working on squaring up HD 829 time is now 0\n",
      "working on squaring up HD 1411 time is now 0\n",
      "working on squaring up HD 1890 time is now 0\n",
      "working on squaring up HD 3757 time is now 1\n",
      "working on squaring up HD 4439 time is now 1\n",
      "working on squaring up HD 5494 time is now 1\n",
      "working on squaring up HD 6757 time is now 2\n",
      "working on squaring up HD 6978 time is now 2\n",
      "working on squaring up HD 7275 time is now 2\n",
      "working on squaring up HD 7483 time is now 3\n",
      "working on squaring up HD 8236 time is now 3\n",
      "We have attempted to address underpop in a total of 1181 HDs\n",
      "... but we got stuck in 95\n",
      "Here is a scatterplot of original (x) to final pop (y)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING UP UNDERPOPPED\n",
    "HDnAddedUnits, HDaddedPop = [0]*nHDs, [0.]*nHDs\n",
    "underPoppedList, stillUnderPoppedList = list(),list()\n",
    "#minDistrictPop, maxDistrictPop = 0.95 * aDP, 1.05 * aDP  #should have been previously defined\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop < minDistrictPop and t in popHDlist:\n",
    "        underPoppedList.append(t)\n",
    "maxGap = maxDistrictPop - aDP \n",
    "print(\"We will now square up the\",len(underPoppedList),\"HDs with pop <\",int(minDistrictPop),\"to within\",int(maxGap) )\n",
    "startTime = time.time()\n",
    "for ii,t in enumerate(underPoppedList):\n",
    "    if ii%100 == 0:\n",
    "        print(\"working on squaring up HD\",t,\"time is now\",int(time.time() - startTime)) \n",
    "    gap = aDP - HDvPop[t]\n",
    "    origGap = gap\n",
    "    nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "    for u in HDunitList[t]:\n",
    "        for uu in unitNbrs[u]:\n",
    "            if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap:\n",
    "                nearHDlist.append(uu)   #below line: bias toward close, underused\n",
    "                nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  \n",
    "    currList = HDunitList[t].copy()\n",
    "    addedList = list()\n",
    "    stillGoing = True\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd: \n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else:\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap:  \n",
    "                    nearHDlist.append(uu)\n",
    "                    nearHDscore.append((unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )  #bias toward close, underused\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    HDunitList[t] += addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)\n",
    "    HDaddedPop[t] = np.sum( [unitPop[u] for u in addedList] )\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        print(\"Oops! HD\",t,\"now has overshot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after adding\",HDaddedPop[t] )\n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        stillUnderPoppedList.append(t)\n",
    "print(\"We have attempted to address underpop in a total of\",len(underPoppedList),\"HDs\")\n",
    "if len(stillUnderPoppedList) > 0:\n",
    "    print(\"... but we got stuck in\",len(stillUnderPoppedList))\n",
    "print(\"Here is a scatterplot of original (x) to final pop (y)\")\n",
    "plt.scatter([HDvPop[t] - HDaddedPop[t] for t in underPoppedList], [HDvPop[t] for t in underPoppedList])\n",
    "plt.plot([minDistrictPop,maxDistrictPop,maxDistrictPop,minDistrictPop],[minDistrictPop,minDistrictPop,maxDistrictPop,maxDistrictPop],lw=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "72398364-3e4a-4a03-a2cf-58a21bdea412",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "previous unit avg and sd usage are 0.99864 0.08276\n",
      "amped unit avg and sd usage are 1.00477 0.08601\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prevUnitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in latestHDlist[t]:\n",
    "        prevUnitUse[u] += HDweight[t] * nDistricts\n",
    "\n",
    "ampedUnitUse = [0. for u in range(nUnits)]\n",
    "ampedHDlist = [HDunitList[t].copy() for t in range(nHDs)]\n",
    "ampedHDpop = [HDvPop[t] for t in range(nHDs) ]\n",
    "for t in popHDlist:\n",
    "    for u in ampedHDlist[t]:\n",
    "        ampedUnitUse[u] += HDweight[t] * nDistricts   #KISS - recalc these\n",
    "plt.hist(prevUnitUse,bins=50,weights=unitPop,label=\"previous\",histtype=\"step\")\n",
    "plt.hist(ampedUnitUse, bins=50, weights=unitPop,label=\"amped\",histtype=\"step\")\n",
    "plt.legend()\n",
    "ampedUnitUseAvg, ampedUnitUseSD = getWeightedAvgAndSD(ampedUnitUse,unitPop)\n",
    "print(\"previous unit avg and sd usage are\",r5(latestAvg), r5(latestSD) )\n",
    "print(\"amped unit avg and sd usage are\",r5(ampedUnitUseAvg), r5(ampedUnitUseSD) )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "f703cd30-fc17-4e4e-a458-40d9c74b266b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\n",
      "Let's now square down the 2198 overPopped HDs to within 3933\n",
      "working on squaring down HD 4 time is now 0\n",
      "working on squaring down HD 846 time is now 1\n",
      "working on squaring down HD 1724 time is now 2\n",
      "working on squaring down HD 2288 time is now 4\n",
      "working on squaring down HD 3056 time is now 5\n",
      "working on squaring down HD 3844 time is now 6\n",
      "can't drop any more units to HD 4158 without creating an enclave\n",
      "can't drop any more units to HD 4538 without creating an enclave\n",
      "working on squaring down HD 4604 time is now 7\n",
      "can't drop any more units to HD 4810 without creating an enclave\n",
      "can't drop any more units to HD 4832 without creating an enclave\n",
      "can't drop any more units to HD 4834 without creating an enclave\n",
      "can't drop any more units to HD 4987 without creating an enclave\n",
      "can't drop any more units to HD 5035 without creating an enclave\n",
      "working on squaring down HD 5511 time is now 8\n",
      "working on squaring down HD 6399 time is now 9\n",
      "can't drop any more units to HD 6520 without creating an enclave\n",
      "working on squaring down HD 7135 time is now 10\n",
      "working on squaring down HD 7964 time is now 11\n",
      "can't drop any more units to HD 8583 without creating an enclave\n",
      "We have attempted to address overpop in a total of 2198 HDs\n",
      "... but we got stuck in 191\n",
      "Here is a scatterplot of original to final pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#SQUARING DOWN\n",
    "print(\"Continuing with narrowing the distro.  This loop: remove overused units from overpopped HDs\")\n",
    "HDnShedUnits = [0]*nHDs\n",
    "overPoppedList, stillOverPoppedList = list(), list()\n",
    "startTime = time.time()\n",
    "for t,pop in enumerate(HDvPop):\n",
    "    if pop > maxDistrictPop and t in popHDlist:\n",
    "        overPoppedList.append(t)\n",
    "\n",
    "maxGap = aDP - minDistrictPop   \n",
    "print(\"Let's now square down the\",len(overPoppedList),\"overPopped HDs to within\", int(maxGap))   \n",
    "for ii,t in enumerate(overPoppedList):\n",
    "    if ii%200 == 0:\n",
    "        print(\"working on squaring down HD\",t,\"time is now\",int(time.time()-startTime))\n",
    "    unbroken, noEnclave, sPL, ePL = enclaveCheck(HDunitList[t],unitNbrs)\n",
    "    if not (unbroken and noEnclave):\n",
    "        print(\"SKIPPING shedding attempt on overpopped HD\",t,\"with pop\",HDvPop[t],\"because it was not legal to start\")\n",
    "    else:\n",
    "        excess = HDvPop[t] - aDP\n",
    "        origExcess = excess\n",
    "        HDboundaryList, HDboundaryScore = list(), list()  #these will be dynamic lists of the overused border units to jettison\n",
    "        #distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        starterU = homeU[t] #HDunitList[t][distList.index(np.min(distList))] #update from vanillaHD\n",
    "        barredSet = barredJettisonSet.union({starterU})\n",
    "        for u in set(HDunitList[t]).difference(barredSet):\n",
    "            isBoundary = False\n",
    "            for uu in unitNbrs[u]:\n",
    "                if uu not in HDunitList[t]:\n",
    "                    isBoundary = True\n",
    "                    break\n",
    "            if isBoundary and HDvPop[t] - unitPop[u] > aDP - maxGap:  #shedding this unit won't send us too far under targetpop\n",
    "                HDboundaryList.append(u)\n",
    "                HDboundaryScore.append((1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t])  #low-score = bias toward far, overused\n",
    "        currList = HDunitList[t].copy()\n",
    "        shedList, stillGoing = list(), True\n",
    "        while excess > maxGap and len(HDboundaryList) > 0 and stillGoing:   #shed the highest-scoring neighboring overused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(HDboundaryScore), 0, True\n",
    "            while i < len(HDboundaryScore) and notYetPicked and stillGoing:        \n",
    "                listNo = idx[i]   #low (large negative) score is preferred to shed\n",
    "                unitNoToShed = HDboundaryList[listNo]  # attempt to shed this unit ...\n",
    "                tryList = list(set(currList).difference( {unitNoToShed} ) )\n",
    "                unbroken, noEnclave, sPL, ePL = enclaveCheck(tryList,unitNbrs) \n",
    "                if unbroken and noEnclave:             #... if that won't eff up contiguity\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't drop any more units without creating an enclave\n",
    "                print(\"can't drop any more units to HD\",t,\"without creating an enclave\")\n",
    "            else: #add this eligible unit\n",
    "                shedList.append(unitNoToShed)\n",
    "                excess -= unitPop[unitNoToShed]        \n",
    "                del currList[currList.index(unitNoToShed) ]\n",
    "                del HDboundaryList[listNo]\n",
    "                del HDboundaryScore[listNo]\n",
    "                for u in unitNbrs[unitNoToShed]:      # ... and ID any neighboring units that will now be on boundary after we shed this unit\n",
    "                    if u in currList and u not in HDboundaryList and (unitPop[u] <= excess + maxGap and u not in barredSet): \n",
    "                        HDboundaryList.append(u)\n",
    "                        HDboundaryScore.append( (1. - unitUse[u]) - unitCP[u].distance(hdCP[t]) / avgDist[t] )  #low-score, bias toward far & overused       \n",
    "                for uu in HDboundaryList.copy():\n",
    "                    if unitPop[uu] > excess + maxGap:  #checking to see if the latest pop change DQ's any large units on current boundary\n",
    "                        del HDboundaryScore[HDboundaryList.index(uu)]\n",
    "                        del HDboundaryList[HDboundaryList.index(uu)]   \n",
    "        for u in shedList:\n",
    "            unitUse[u] -= HDweight[t] * nDistricts\n",
    "        HDunitList[t], HDvPop[t] = currList.copy(), np.sum( [unitPop[u] for u in currList ] )    \n",
    "        if HDvPop[t] < minDistrictPop:\n",
    "            print(\"Oops! HD\",t,\"now has undershot pop =\",int(HDvPop[t]),\"not\",int(aDP),\"after shedding\",\n",
    "                 np.sum([unitPop[u] for u in shedList]) )        \n",
    "        if HDvPop[t] > maxDistrictPop:\n",
    "            stillOverPoppedList.append(t)\n",
    "        HDnShedUnits[t] = -1 * len(shedList)\n",
    "        if homeU[t] not in HDunitList[t]:\n",
    "            print(\"WARNING: we lost the home unit\",homeU[t],\"from HD\",HD)\n",
    "            lostHomeUlist.append(t)\n",
    "            \n",
    "print(\"We have attempted to address overpop in a total of\",len(overPoppedList),\"HDs\")\n",
    "if len(stillOverPoppedList) > 0:\n",
    "    print(\"... but we got stuck in\",len(stillOverPoppedList))\n",
    "print(\"Here is a scatterplot of original to final pop\")\n",
    "plt.scatter([ampedHDpop[t] for t in overPoppedList], [HDvPop[t] for t in overPoppedList])\n",
    "plt.axvline(aDP, 0.9*aDP, 1.1*aDP, ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "9c4d12b3-8596-4a5b-937e-7ef8ed71a903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating our unitUse, underpopped and overpopped lists\n",
      "unit use histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit use by pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we now have a total of 95 underpopped HDs and 191 overpopped HDs\n"
     ]
    }
   ],
   "source": [
    "print(\"updating our unitUse, underpopped and overpopped lists\")\n",
    "unitUse = [0.]*nUnits\n",
    "HDvPop = [0.]*nHDs\n",
    "stillUnder, stillOver = list(), list()\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        stillUnder.append(t)\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        stillOver.append(t)\n",
    "print(\"unit use histogram\")\n",
    "plt.hist(unitUse, weights = unitPop)\n",
    "plt.show()\n",
    "print(\"unit use by pop\")\n",
    "plt.scatter(unitPop,unitUse)\n",
    "plt.show()\n",
    "print(\"we now have a total of\",len(stillUnder),\"underpopped HDs and\",len(stillOver),\"overpopped HDs\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "c7b7fff8-d72b-48b6-a1f3-e131d23f1adb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([unitPop[homeU[t]] for t in stillUnder],histtype =\"step\",label=\"underPopped\")\n",
    "plt.hist([unitPop[homeU[t]] for t in stillOver],histtype =\"step\",label=\"overPopped\")\n",
    "plt.xlabel(\"homeU pop\")\n",
    "plt.legend()\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "3e80ff56-7d63-4aca-b84a-bae0cdc3d835",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are centerpoints of under (small, faint) and overpopped HDs\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are centerpoints of under (small, faint) and overpopped HDs\")\n",
    "for t in stillOver:\n",
    "    plotPoly(HDCP[t].buffer(0.03),0.5)\n",
    "for t in stillUnder:\n",
    "    plotPoly(HDCP[t].buffer(0.01),1)\n",
    "for c in range(nCounties):\n",
    "    plotPoly(countyGeom[c],0.1)\n",
    "#for u in range(nUnits):\n",
    "#    if unitUse[u] > 1.2 :\n",
    "#        plotPoly(unitGeom[u],0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "f65ac44d-8755-4ff0-bad3-a1e79cf65d9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets zoom in on SW Ohio, then Akron-Cleve area\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"lets zoom in on SW Ohio, then Akron-Cleve area\")\n",
    "for u in range(nUnits):\n",
    "    if unitCPx[u] < -84 and unitCPy[u] < 39.9:\n",
    "        plotPoly(unitGeom[u], 0.1)\n",
    "        if unitPop[u] > 0.05 * aDP:\n",
    "            plotCenter(r3(unitPop[u]/aDP),unitCP[u],8)\n",
    "for t in stillUnder:\n",
    "    if  HDCPx[t] < -84 and HDCPy[t] < 39.9:\n",
    "        plotCenter(\"u\",HDCP[t],6)\n",
    "for t in stillOver:\n",
    "    if  HDCPx[t] < -84 and HDCPy[t] < 39.9:\n",
    "        plotCenter(\"o\",HDCP[t],6)\n",
    "plt.show()\n",
    "\n",
    "for u in range(nUnits):\n",
    "    if unitCP[u].distance(Point(-81.5,41.4)) < 0.7:\n",
    "        plotPoly(unitGeom[u], 0.1)\n",
    "        if unitPop[u] > 0.05 * aDP:\n",
    "            plotCenter(r3(unitPop[u]/aDP),unitCP[u],8)\n",
    "for t in stillUnder:\n",
    "    if  HDCP[t].distance(Point(-81.5,41.4)) < 0.7:\n",
    "        plotCenter(\"u\",HDCP[t],6)\n",
    "for t in stillOver:\n",
    "    if  HDCP[t].distance(Point(-81.5,41.4)) < 0.7:\n",
    "        plotCenter(\"o\",HDCP[t],6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "37944660-9197-4e58-ac32-0a2bdad79616",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.005000000000000038\n"
     ]
    }
   ],
   "source": [
    "print(maxGap/aDP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "703d18f8-eccb-411f-b295-e66405ca328c",
   "metadata": {},
   "outputs": [],
   "source": [
    "saved2unitList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping\n",
    "#HDunitList = [saved2unitList[t].copy() for t in range(nHDs)] #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "9c07f7b4-cff5-4964-a792-bf80d474444f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min, max uses are 0.806976733148653 1.3771839157217378\n"
     ]
    }
   ],
   "source": [
    "HDunitList = [saved2unitList[t].copy() for t in range(nHDs)] #restart\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "print(\"min, max uses are\",np.min(unitUse),np.max(unitUse) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "842d6044-839b-406f-a7f9-eb8c90cc51ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lets check that we really couldn't add another unit to the still unders\n",
      "of 95 stillUnder, we can fix 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Lets check that we really couldn't add another unit to the still unders\")\n",
    "fixableUnderSet = set()\n",
    "for v in stillUnder:\n",
    "    adjoiners = getAdjoiners(HDunitList[t], unitNbrs)\n",
    "    canAdd = False\n",
    "    for u in adjoiners:\n",
    "        if abs(HDvPop[t] + unitPop[u] - aDP) < maxGap:\n",
    "            canAdd  = wontEnclave(u, HDunitList[t],unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                fixableUnderSet.add(v)\n",
    "                break\n",
    "        if canAdd:\n",
    "            break\n",
    "print(\"of\",len(stillUnder),\"stillUnder, we can fix\",len(fixableUnderSet) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "3e552ae0-9170-418b-b635-f60193f27000",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, what if we drop a single overused unit from these stillUnders, then adding others back in?\n",
      "I fixed stillUnder 981 its final pop - aDP is -496\n",
      "I fixed stillUnder 1206 its final pop - aDP is 3246\n",
      "I fixed stillUnder 1233 its final pop - aDP is 3199\n",
      "I fixed stillUnder 1247 its final pop - aDP is -1025\n",
      "I fixed stillUnder 1258 its final pop - aDP is -1025\n",
      "I fixed stillUnder 1278 its final pop - aDP is 3246\n",
      "I fixed stillUnder 1321 its final pop - aDP is -1025\n",
      "I fixed stillUnder 1341 its final pop - aDP is -1025\n",
      "I fixed stillUnder 1375 its final pop - aDP is -120\n",
      "I fixed stillUnder 1403 its final pop - aDP is 3246\n",
      "I fixed stillUnder 1427 its final pop - aDP is -1765\n",
      "I fixed stillUnder 1465 its final pop - aDP is -1025\n",
      "I fixed stillUnder 1473 its final pop - aDP is 2961\n",
      "I fixed stillUnder 1481 its final pop - aDP is 3246\n",
      "I fixed stillUnder 1483 its final pop - aDP is -1025\n",
      "I fixed stillUnder 1521 its final pop - aDP is -1025\n",
      "I fixed stillUnder 1618 its final pop - aDP is 2961\n",
      "I fixed stillUnder 1627 its final pop - aDP is -1765\n",
      "I fixed stillUnder 1739 its final pop - aDP is -120\n",
      "I fixed stillUnder 1749 its final pop - aDP is -120\n",
      "I fixed stillUnder 1758 its final pop - aDP is 3199\n",
      "I fixed stillUnder 1870 its final pop - aDP is 3199\n",
      "I fixed stillUnder 3779 its final pop - aDP is 2487\n",
      "I fixed stillUnder 3789 its final pop - aDP is 2487\n",
      "I fixed stillUnder 3800 its final pop - aDP is -2939\n",
      "I fixed stillUnder 3903 its final pop - aDP is 2656\n",
      "I fixed stillUnder 3915 its final pop - aDP is 2656\n",
      "I fixed stillUnder 3946 its final pop - aDP is 2656\n",
      "I fixed stillUnder 3981 its final pop - aDP is 2656\n",
      "I fixed stillUnder 3989 its final pop - aDP is 3295\n",
      "I fixed stillUnder 4023 its final pop - aDP is 2656\n",
      "I fixed stillUnder 4038 its final pop - aDP is 3091\n",
      "I fixed stillUnder 4050 its final pop - aDP is 2140\n",
      "I fixed stillUnder 4127 its final pop - aDP is 3295\n",
      "I fixed stillUnder 4128 its final pop - aDP is 3295\n",
      "I fixed stillUnder 4129 its final pop - aDP is 3295\n",
      "I fixed stillUnder 4152 its final pop - aDP is -653\n",
      "I fixed stillUnder 4153 its final pop - aDP is 3193\n",
      "I fixed stillUnder 4181 its final pop - aDP is -2939\n",
      "I fixed stillUnder 4199 its final pop - aDP is 2487\n",
      "I fixed stillUnder 4228 its final pop - aDP is -2939\n",
      "I fixed stillUnder 4229 its final pop - aDP is -2939\n",
      "I fixed stillUnder 4238 its final pop - aDP is -2939\n",
      "I fixed stillUnder 4249 its final pop - aDP is 3013\n",
      "I fixed stillUnder 4268 its final pop - aDP is 3295\n",
      "I fixed stillUnder 4276 its final pop - aDP is 3295\n",
      "I fixed stillUnder 4282 its final pop - aDP is 724\n",
      "I fixed stillUnder 4296 its final pop - aDP is 3295\n",
      "I fixed stillUnder 4305 its final pop - aDP is 3295\n",
      "I fixed stillUnder 4308 its final pop - aDP is 2487\n",
      "I fixed stillUnder 4322 its final pop - aDP is 2487\n",
      "I fixed stillUnder 4371 its final pop - aDP is 2487\n",
      "I fixed stillUnder 4534 its final pop - aDP is -3756\n",
      "I fixed stillUnder 4542 its final pop - aDP is -2939\n",
      "I fixed stillUnder 4543 its final pop - aDP is 1961\n",
      "I fixed stillUnder 4566 its final pop - aDP is 3295\n",
      "I fixed stillUnder 4636 its final pop - aDP is 2487\n",
      "I fixed stillUnder 4654 its final pop - aDP is 2487\n",
      "I fixed stillUnder 4800 its final pop - aDP is 2487\n",
      "I fixed stillUnder 4801 its final pop - aDP is 2487\n",
      "I fixed stillUnder 4805 its final pop - aDP is 2487\n",
      "I fixed stillUnder 4806 its final pop - aDP is -12\n",
      "I fixed stillUnder 4837 its final pop - aDP is -2604\n",
      "I fixed stillUnder 4872 its final pop - aDP is 2487\n",
      "I fixed stillUnder 4988 its final pop - aDP is 518\n",
      "I fixed stillUnder 4994 its final pop - aDP is 2487\n",
      "I fixed stillUnder 4995 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5001 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5003 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5004 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5005 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5006 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5007 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5012 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5049 its final pop - aDP is -2939\n",
      "I fixed stillUnder 5116 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5122 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5125 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5129 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5144 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5154 its final pop - aDP is 2487\n",
      "I fixed stillUnder 5155 its final pop - aDP is 1164\n",
      "I fixed stillUnder 6822 its final pop - aDP is 674\n",
      "I fixed stillUnder 6829 its final pop - aDP is 674\n",
      "I fixed stillUnder 6837 its final pop - aDP is 2586\n",
      "I fixed stillUnder 7023 its final pop - aDP is 674\n",
      "I fixed stillUnder 7176 its final pop - aDP is 674\n",
      "I fixed stillUnder 7185 its final pop - aDP is 674\n",
      "I fixed stillUnder 7200 its final pop - aDP is 674\n",
      "I fixed stillUnder 7214 its final pop - aDP is 674\n",
      "I fixed stillUnder 7220 its final pop - aDP is 674\n",
      "I fixed stillUnder 7223 its final pop - aDP is 674\n",
      "I fixed stillUnder 7229 its final pop - aDP is 674\n",
      "I fixed stillUnder 7267 its final pop - aDP is 674\n",
      "I fixed stillUnder 8028 its final pop - aDP is 674\n",
      "95 95 fixed, total tried\n"
     ]
    }
   ],
   "source": [
    "print(\"OK, what if we drop a single overused unit from these stillUnders, then adding others back in?\")\n",
    "fixableUnderSet = set()\n",
    "for t in stillUnder:\n",
    "    adjoiners = getAdjoiners(HDunitList[t], unitNbrs)\n",
    "    edgeUset =  set(getAdjoiners(adjoiners, unitNbrs)).intersection(set(HDunitList[t])).difference({homeU[t]}) \n",
    "    edgeUs = list()\n",
    "    for u in edgeUset: #checking that we maintain contiguity dropping this u\n",
    "        unbroken, noEnclave, sPL, ePL = enclaveCheck(list( set(HDunitList[t]).difference({u}) ) ,unitNbrs)\n",
    "        if unbroken and noEnclave:\n",
    "            edgeUs.append(u)  #we only consider dropping a boundary unit that preserves contig'y\n",
    "    edgeUses = [-1.*unitUse[u] for u in edgeUs]\n",
    "    cantFix, idxxx = True, np.argsort(edgeUses)\n",
    "    for jj in range(len(edgeUs)) :\n",
    "        iNo = idxxx[jj]\n",
    "        tryU = edgeUs[iNo]\n",
    "        contigUs, contigPop = list(set(HDunitList[t]).difference( {tryU} ) ), HDvPop[t] - unitPop[tryU]\n",
    "        gap = aDP - contigPop\n",
    "        origGap = gap\n",
    "        currList, addedList = contigUs.copy(), list()\n",
    "        adjSet, adjoiners =  set( getAdjoiners(currList, unitNbrs) ).difference({tryU}) , list()\n",
    "        for uu in adjSet:\n",
    "            if contigPop + unitPop[uu] < maxDistrictPop:\n",
    "                adjoiners.append(uu)\n",
    "        nearHDlist = [uu for uu in adjoiners]  \n",
    "        nearHDscore = [ unitUse[uu]  for uu in adjoiners ]  #simpler scoring system, only adding a few\n",
    "        stillGoing = True\n",
    "    \n",
    "        while gap > maxGap and len(nearHDlist) > 0 and stillGoing and cantFix:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "            idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "            while i < len(nearHDscore) and notYetPicked:        \n",
    "                listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "                canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "                if canAdd:\n",
    "                    notYetPicked = False\n",
    "                else:\n",
    "                    i +=1\n",
    "            if notYetPicked:\n",
    "                stillGoing = False  #can't add any more units without creating an enclave\n",
    "            else: #we selected the best unit to add legally\n",
    "                gap -= unitPop[unitNoToAdd]\n",
    "                contigPop += unitPop[unitNoToAdd]\n",
    "                addedList.append(unitNoToAdd)\n",
    "                currList.append( unitNoToAdd)\n",
    "                for uu in unitNbrs[unitNoToAdd]: # ... and add its nonHD, nonwhole neighbors to future candidates\n",
    "                    if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap and uu not in {tryU}:  \n",
    "                        nearHDlist.append(uu)                           #subbed HDcircle for HDpoly in below\n",
    "                        nearHDscore.append(1.1 * unitUse[u]) #1.1 to encourage original neighbor pickup \n",
    "                del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                for i, uu in enumerate(nearHDlist.copy()):\n",
    "                    if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                        del nearHDscore[nearHDlist.index(uu)]\n",
    "                        del nearHDlist[ nearHDlist.index(uu)]\n",
    "\n",
    "        if abs(gap) < maxGap :\n",
    "            print(\"I fixed stillUnder\",t,\"its final pop - aDP is\",int(contigPop - aDP))\n",
    "            cantFix = False\n",
    "            fixableUnderSet.add(t)\n",
    "            for u in addedList:\n",
    "                unitUse[u] += HDweight[t] * nDistricts\n",
    "            for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "                unitUse[u] -= HDweight[t] * nDistricts       \n",
    "            HDunitList[t] = contigUs + addedList\n",
    "            HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "            break\n",
    "\n",
    "print(len(fixableUnderSet), len(stillUnder),\"fixed, total tried\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "a9023a47-cdaa-47ef-91aa-156678490aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "noMoreUnderList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "a0688cdb-eabe-4aa8-a520-8dc121721866",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDunitList = [noMoreUnderList[t].copy() for t in range(nHDs)] #restart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "7dbe8d70-907e-4b4a-a42e-9fa60d4d7cd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current relative pop.  Total number of stillOver = 191\n"
     ]
    },
    {
     "data": {
      "image/png": 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49b4gTi6XK9rtAADgbNBJS0uTJNXX14fNr6+vt5elpaWpoaEhbHlbW5saGxvDak63jn/cxhfFxcXJ6/WGTQAA4PzmaNDJyMhQWlqaKioq7HnBYFDV1dXKzs6WJGVnZ+vIkSOqqamxazZt2qRQKKSsrCy7prKyUq2trXZNeXm5Bg0adNrTVugamtvaNX/9e5q//j0eAQEA6BI6HHSOHTum2tpa1dbWSvr7Bci1tbU6dOiQXC6XZs6cqQceeEAvvfSSdu3apZ/+9Kfy+XyaPHmyJGnIkCGaMGGCpk+frq1bt+rtt99WcXGxpk6dKp/PJ0m66aab5PF4VFhYqN27d+uFF17Q0qVLNWvWLMd2HM5rD1n6jy0f6T+2fMQjIAAAXUKHL0bevn27rr32Wvv9yfBRUFCgNWvW6K677lJTU5NmzJihI0eO6IorrlBZWZni4+Ptz5SWlqq4uFjjxo2T2+3WlClTtGzZMnt5YmKiXnvtNRUVFWn06NHq06ePFixYwE/LAQBAh7gsyzLyT+9gMKjExEQFAgHHr9cZMPcVR9d3rju4KE+SdLylTUMXbJQk7bkvV909jv6oDwBwHnD6+9uYX10BAAB8EUEHAAAYi6ADAACMRdABAADG4mpROCY+NkZv3nWt/RoAgGgj6MAxbrdL6cndo90GAAA2Tl0BAABjcUQHjmlpC+nR1/ZJkn49fpA8seRoAEB08U0Ex7SFQlpd+aFWV36otlAo2u0AAEDQAQAA5iLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFvfRgWPiY2P02h1X2a8BAIg2gg4c43a79J3UntFuAwAAG6euAACAsTiiA8e0tIW04vUPJElF136bR0AAAKKOoAPHtIVCWlqxX5L0i6u/KQ8HDAEAUcY3EQAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsfh5ORwTFxujPxWNtV8DABBtBB04Jsbt0sj0pGi3AQCAjVNXAADAWBzRgWNa2kJ65u0DkqSbx2bwCAgAQNQRdOCYtlBIJa/+RZI0Lbs/j4AAAEQd30QAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMZyPOi0t7dr/vz5ysjIUEJCgr71rW/p/vvvl2VZdo1lWVqwYIH69u2rhIQE5eTkaP/+/WHraWxsVH5+vrxer5KSklRYWKhjx4453S4cFBcbo/+cfrn+c/rlPAICANAlOB50Hn74Ya1cuVJPPPGE9u7dq4cffliLFy/W8uXL7ZrFixdr2bJlWrVqlaqrq9WjRw/l5ubqxIkTdk1+fr52796t8vJybdiwQZWVlZoxY4bT7cJBMW6Xsr/VW9nf6q0Ytyva7QAAIJf1j4daHPC9731Pqamp+t3vfmfPmzJlihISEvTss8/Ksiz5fD7deeed+vWvfy1JCgQCSk1N1Zo1azR16lTt3btXQ4cO1bZt25SZmSlJKisr06RJk/Txxx/L5/N9aR/BYFCJiYkKBALyer1O7qIGzH3F0fWd6w4uyot2CwAAQzj9/e34EZ3vfve7qqio0Pvvvy9J+vOf/6y33npLEydOlCQdOHBAfr9fOTk59mcSExOVlZWlqqoqSVJVVZWSkpLskCNJOTk5crvdqq6uPu12m5ubFQwGwyZ0rtb2kH5fdVC/rzqo1vZQtNsBAMD5R0DMnTtXwWBQgwcPVkxMjNrb2/Xggw8qPz9fkuT3+yVJqampYZ9LTU21l/n9fqWkpIQ3Ghur5ORku+aLSkpKtHDhQqd3Bx3Q2h7Sgj/tliTdMPoidYvhWncAQHQ5/k304osvqrS0VM8995x27NihtWvX6tFHH9XatWud3lSYefPmKRAI2FNdXV1EtwcAALo+x4/ozJ49W3PnztXUqVMlScOHD9dHH32kkpISFRQUKC0tTZJUX1+vvn372p+rr6/XJZdcIklKS0tTQ0ND2Hrb2trU2Nhof/6L4uLiFBcX5/TuAACAc5jjR3SOHz8utzt8tTExMQqF/n7NRkZGhtLS0lRRUWEvDwaDqq6uVnZ2tiQpOztbR44cUU1NjV2zadMmhUIhZWVlOd0yAAAwlONHdK6//no9+OCD6tevn4YNG6Z3331XS5Ys0c9//nNJksvl0syZM/XAAw9o4MCBysjI0Pz58+Xz+TR58mRJ0pAhQzRhwgRNnz5dq1atUmtrq4qLizV16tSz+sUVAACAFIGgs3z5cs2fP1+/+tWv1NDQIJ/Pp1/84hdasGCBXXPXXXepqalJM2bM0JEjR3TFFVeorKxM8fHxdk1paamKi4s1btw4ud1uTZkyRcuWLXO6XQAAYDDH76PTVXAfnc5z8j46x1vaNHTBRknSnvty1d3jeI4GABjO6e9vvongGE+MW0//LNN+DQBAtBF04JjYGLeuG5z65YUAAHQS/uwGAADG4ogOHNPaHtL6dz+RJE2+9BvcGRkAEHUEHTimtT2k2X/cKUnKG9GXoAMAiDq+iQAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjMXPy+EYT4xbK24aZb8GACDaCDpwTGyMW3kj+ka7DQAAbPzZDQAAjMURHTimrT2kjbvrJUm5w1IVy+krAECUEXTgmJb2kIqe2yFJ2nNfLkEHABB1fBMBAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLn5fDMd1i3HrkhhH2awAAoo2gA8d0i3HrR5np0W4DAAAbf3YDAABjcUQHjmlrD6ly/6eSpKsGXsidkQEAUUfQgWNa2kP6+ZrtkngEBACga+CbCAAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWPy8HI7pFuPWfT8YZr8GACDaCDpwTLcYt36aPSDabQAAYOPPbgAAYCyO6MAx7SFLWw80SpLGZCQrxu2KckcAgPMdQQdf24C5r5x17cFFeRHsBACAcBE5dfXJJ5/oJz/5iXr37q2EhAQNHz5c27dvt5dblqUFCxaob9++SkhIUE5Ojvbv3x+2jsbGRuXn58vr9SopKUmFhYU6duxYJNoFAACGcjzofPbZZxo7dqy6deumV199VXv27NFvfvMb9erVy65ZvHixli1bplWrVqm6ulo9evRQbm6uTpw4Ydfk5+dr9+7dKi8v14YNG1RZWakZM2Y43S4AADCYy7Isy8kVzp07V2+//bbefPPN0y63LEs+n0933nmnfv3rX0uSAoGAUlNTtWbNGk2dOlV79+7V0KFDtW3bNmVmZkqSysrKNGnSJH388cfy+Xxf2kcwGFRiYqICgYC8Xq9zO6iOnapBOE5dAQDOxOnvb8eP6Lz00kvKzMzUj370I6WkpOjSSy/VU089ZS8/cOCA/H6/cnJy7HmJiYnKyspSVVWVJKmqqkpJSUl2yJGknJwcud1uVVdXn3a7zc3NCgaDYRMAADi/OR50PvzwQ61cuVIDBw7Uxo0bdcstt+i2227T2rVrJUl+v1+SlJqaGva51NRUe5nf71dKSkrY8tjYWCUnJ9s1X1RSUqLExER7Sk9Pd3rXAADAOcbxoBMKhTRq1Cg99NBDuvTSSzVjxgxNnz5dq1atcnpTYebNm6dAIGBPdXV1Ed0eAADo+hwPOn379tXQoUPD5g0ZMkSHDh2SJKWlpUmS6uvrw2rq6+vtZWlpaWpoaAhb3tbWpsbGRrvmi+Li4uT1esMmAABwfnM86IwdO1b79u0Lm/f++++rf//+kqSMjAylpaWpoqLCXh4MBlVdXa3s7GxJUnZ2to4cOaKamhq7ZtOmTQqFQsrKynK6ZQAAYCjHbxh4xx136Lvf/a4eeugh/fjHP9bWrVu1evVqrV69WpLkcrk0c+ZMPfDAAxo4cKAyMjI0f/58+Xw+TZ48WdLfjwBNmDDBPuXV2tqq4uJiTZ069ax+cQUAACBFIOhcdtllWrdunebNm6f77rtPGRkZevzxx5Wfn2/X3HXXXWpqatKMGTN05MgRXXHFFSorK1N8fLxdU1paquLiYo0bN05ut1tTpkzRsmXLnG4XAAAYzPH76HQV3Eena+I+OgCAM+ny99EBAADoKgg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIwV8aCzaNEiuVwuzZw505534sQJFRUVqXfv3rrgggs0ZcoU1dfXh33u0KFDysvLU/fu3ZWSkqLZs2erra0t0u0CAACDRDTobNu2TU8++aRGjBgRNv+OO+7Qyy+/rD/84Q/avHmzDh8+rB/+8If28vb2duXl5amlpUXvvPOO1q5dqzVr1mjBggWRbBcAABgmYkHn2LFjys/P11NPPaVevXrZ8wOBgH73u99pyZIluu666zR69Gg988wzeuedd7RlyxZJ0muvvaY9e/bo2Wef1SWXXKKJEyfq/vvv14oVK9TS0hKplgEAgGEiFnSKioqUl5ennJycsPk1NTVqbW0Nmz948GD169dPVVVVkqSqqioNHz5cqampdk1ubq6CwaB279592u01NzcrGAyGTQAA4PwWG4mVPv/889qxY4e2bdt2yjK/3y+Px6OkpKSw+ampqfL7/XbNP4ack8tPLjudkpISLVy40IHuAQCAKRw/olNXV6fbb79dpaWlio+Pd3r1/9S8efMUCATsqa6urtO2DQAAuibHg05NTY0aGho0atQoxcbGKjY2Vps3b9ayZcsUGxur1NRUtbS06MiRI2Gfq6+vV1pamiQpLS3tlF9hnXx/suaL4uLi5PV6wyYAAHB+czzojBs3Trt27VJtba09ZWZmKj8/337drVs3VVRU2J/Zt2+fDh06pOzsbElSdna2du3apYaGBrumvLxcXq9XQ4cOdbplAABgKMev0enZs6cuvvjisHk9evRQ79697fmFhYWaNWuWkpOT5fV6deuttyo7O1uXX365JGn8+PEaOnSopk2bpsWLF8vv9+vuu+9WUVGR4uLinG4ZAAAYKiIXI3+Zxx57TG63W1OmTFFzc7Nyc3P129/+1l4eExOjDRs26JZbblF2drZ69OihgoIC3XfffdFoFwAAnKNclmVZ0W4iEoLBoBITExUIBBy/XmfA3FccXd/55OCivGi3AADowpz+/uZZVwAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACM5XjQKSkp0WWXXaaePXsqJSVFkydP1r59+8JqTpw4oaKiIvXu3VsXXHCBpkyZovr6+rCaQ4cOKS8vT927d1dKSopmz56ttrY2p9sFAAAGczzobN68WUVFRdqyZYvKy8vV2tqq8ePHq6mpya6544479PLLL+sPf/iDNm/erMOHD+uHP/yhvby9vV15eXlqaWnRO++8o7Vr12rNmjVasGCB0+0CAACDuSzLsiK5gU8//VQpKSnavHmzrrrqKgUCAV144YV67rnndMMNN0iS/vKXv2jIkCGqqqrS5ZdfrldffVXf+973dPjwYaWmpkqSVq1apTlz5ujTTz+Vx+P50u0Gg0ElJiYqEAjI6/U6uk8D5r7i6PrOJwcX5UW7BQBAF+b093fEr9EJBAKSpOTkZElSTU2NWltblZOTY9cMHjxY/fr1U1VVlSSpqqpKw4cPt0OOJOXm5ioYDGr37t2RbhkAABgiNpIrD4VCmjlzpsaOHauLL75YkuT3++XxeJSUlBRWm5qaKr/fb9f8Y8g5ufzkstNpbm5Wc3Oz/T4YDDq1GwAA4BwV0SM6RUVFeu+99/T8889HcjOS/n4RdGJioj2lp6dHfJsAAKBri1jQKS4u1oYNG/T666/roosusuenpaWppaVFR44cCauvr69XWlqaXfPFX2GdfH+y5ovmzZunQCBgT3V1dQ7uDQAAOBc5HnQsy1JxcbHWrVunTZs2KSMjI2z56NGj1a1bN1VUVNjz9u3bp0OHDik7O1uSlJ2drV27dqmhocGuKS8vl9fr1dChQ0+73bi4OHm93rAJAACc3xy/RqeoqEjPPfec/vSnP6lnz572NTWJiYlKSEhQYmKiCgsLNWvWLCUnJ8vr9erWW29Vdna2Lr/8cknS+PHjNXToUE2bNk2LFy+W3+/X3XffraKiIsXFxTndMgAAMJTjQWflypWSpGuuuSZs/jPPPKOf/exnkqTHHntMbrdbU6ZMUXNzs3Jzc/Xb3/7Wro2JidGGDRt0yy23KDs7Wz169FBBQYHuu+8+p9sFAAAGi/h9dKKF++h0TdxHBwBwJufcfXQAAACihaADAACMRdABAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYKzYaDeA88uAua90qP7gorwIdQIAOB9wRAcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFgEHQAAYCyCDgAAMBZBBwAAGIugAwAAjEXQAQAAxiLoAAAAYxF0AACAsQg6AADAWAQdAABgLIIOAAAwFkEHAAAYi6ADAACMRdABAADGIugAAABjxUa7AeBMBsx95axrDy7Ki2AnAIBzEUd0AACAsbp00FmxYoUGDBig+Ph4ZWVlaevWrdFuCQAAnEO6bNB54YUXNGvWLN1zzz3asWOHRo4cqdzcXDU0NES7NQAAcI7oskFnyZIlmj59um6++WYNHTpUq1atUvfu3fX0009HuzUAAHCO6JIXI7e0tKimpkbz5s2z57ndbuXk5Kiqquq0n2lublZzc7P9PhAISJKCwaDj/YWajzu+Tnx9kfjfOtIuvmfjWde+tzA3gp0AQNdw8r/llmU5sr4uGXT+9re/qb29XampqWHzU1NT9Ze//OW0nykpKdHChQtPmZ+enh6RHtH1JD4e7Q4iy/T9A4B/dPToUSUmJn7t9XTJoPNVzJs3T7NmzbLfh0IhNTY2qnfv3nK5XFHsLLKCwaDS09NVV1cnr9cb7Xa6HMbnzBifM2N8zozxOTPG58z+2fhYlqWjR4/K5/M5sp0uGXT69OmjmJgY1dfXh82vr69XWlraaT8TFxenuLi4sHlJSUmRarHL8Xq9/B/pDBifM2N8zozxOTPG58wYnzM73fg4cSTnpC55MbLH49Ho0aNVUVFhzwuFQqqoqFB2dnYUOwMAAOeSLnlER5JmzZqlgoICZWZmasyYMXr88cfV1NSkm2++OdqtAQCAc0SXDTo33nijPv30Uy1YsEB+v1+XXHKJysrKTrlA+XwXFxene+6555TTdvg7xufMGJ8zY3zOjPE5M8bnzDprfFyWU7/fAgAA6GK65DU6AAAATiDoAAAAYxF0AACAsQg6AADAWASdLqayslLXX3+9fD6fXC6X1q9f/6WfeeONNzRq1CjFxcXp29/+ttasWXNKzYoVKzRgwADFx8crKytLW7dudb75CIvE2HyVdXZlkRijkpISXXbZZerZs6dSUlI0efJk7du3LzI7EEGRGJuVK1dqxIgR9g3PsrOz9eqrr0ZmByIsUv/tOWnRokVyuVyaOXOmYz13pkiMz7333iuXyxU2DR48ODI7EGGR+vfzySef6Cc/+Yl69+6thIQEDR8+XNu3b+9QbwSdLqapqUkjR47UihUrzqr+wIEDysvL07XXXqva2lrNnDlT//Zv/6aNG///wyJfeOEFzZo1S/fcc4927NihkSNHKjc3Vw0NDZHajYiIxNh0dJ1dXSTGaPPmzSoqKtKWLVtUXl6u1tZWjR8/Xk1NTZHajYiIxNhcdNFFWrRokWpqarR9+3Zdd911+sEPfqDdu3dHajciJhLjc9K2bdv05JNPasSIEU633WkiNT7Dhg3TX//6V3t66623ItF+xEVifD777DONHTtW3bp106uvvqo9e/boN7/5jXr16tWx5ix0WZKsdevWnbHmrrvusoYNGxY278Ybb7Ryc3Pt92PGjLGKiors9+3t7ZbP57NKSkoc7bczOTU2HV3nuSQSY2RZltXQ0GBJsjZv3uxEm1ERqbGxLMvq1auX9e///u9ft8WocnJ8jh49ag0cONAqLy+3rr76auv22293uNvO59T43HPPPdbIkSMj0GF0OTU+c+bMsa644oqv3Q9HdM5xVVVVysnJCZuXm5urqqoqSVJLS4tqamrCatxut3JycuwaU33Z2OCrjVEgEJAkJScnR7S3aOvo2LS3t+v5559XU1PTefGomrMdn6KiIuXl5Z1Sa7qzHZ/9+/fL5/Ppm9/8pvLz83Xo0KHObDNqzmZ8XnrpJWVmZupHP/qRUlJSdOmll+qpp57q8LYIOuc4v99/yt2iU1NTFQwG9fnnn+tvf/ub2tvbT1vj9/s7s9VO92Vjg46PUSgU0syZMzV27FhdfPHFndVmVJzt2OzatUsXXHCB4uLi9Mtf/lLr1q3T0KFDO7vdTnc24/P8889rx44dKikpiUaLUXU245OVlaU1a9aorKxMK1eu1IEDB3TllVfq6NGj0Wi5U53N+Hz44YdauXKlBg4cqI0bN+qWW27RbbfdprVr13ZoW132ERAAup6ioiK999575+x1BJEwaNAg1dbWKhAI6I9//KMKCgq0efPm8yLsnEldXZ1uv/12lZeXKz4+PtrtdEkTJ060X48YMUJZWVnq37+/XnzxRRUWFkaxs64hFAopMzNTDz30kCTp0ksv1XvvvadVq1apoKDgrNfDEZ1zXFpamurr68Pm1dfXy+v1KiEhQX369FFMTMxpa9LS0jqz1U73ZWODjo1RcXGxNmzYoNdff10XXXRRZ7YZFWc7Nh6PR9/+9rc1evRolZSUaOTIkVq6dGlnt9vpvmx8ampq1NDQoFGjRik2NlaxsbHavHmzli1bptjYWLW3t0ep887xVf77k5SUpO985zv64IMPOqPFqDqb8enbt+8pfzAMGTKkw6f3CDrnuOzsbFVUVITNKy8vt68R8Hg8Gj16dFhNKBRSRUWF8dcRfNnY4OzGyLIsFRcXa926ddq0aZMyMjI6u82o+Kr/fkKhkJqbmyPZWpfwZeMzbtw47dq1S7W1tfaUmZmp/Px81dbWKiYmJhptd5qv8u/n2LFj+t///V/17ds30u1F3dmMz9ixY0+5lcX777+v/v37d2xjX/tyZjjq6NGj1rvvvmu9++67liRryZIl1rvvvmt99NFHlmVZ1ty5c61p06bZ9R9++KHVvXt3a/bs2dbevXutFStWWDExMVZZWZld8/zzz1txcXHWmjVrrD179lgzZsywkpKSLL/f3+n793VEYmy+bJ3nmkiM0S233GIlJiZab7zxhvXXv/7Vno4fP97p+/d1RGJs5s6da23evNk6cOCAtXPnTmvu3LmWy+WyXnvttU7fv68rEuPzRefyr64iMT533nmn9cYbb1gHDhyw3n77bSsnJ8fq06eP1dDQ0On793VFYny2bt1qxcbGWg8++KC1f/9+q7S01Orevbv17LPPdqg3gk4X8/rrr1uSTpkKCgosy7KsgoIC6+qrrz7lM5dcconl8Xisb37zm9YzzzxzynqXL19u9evXz/J4PNaYMWOsLVu2RH5nHBaJsfmydZ5rIjFGp1ufpNP+O+vKIjE2P//5z63+/ftbHo/HuvDCC61x48adkyHHsiL3355/dC4HnUiMz4033mj17dvX8ng81je+8Q3rxhtvtD744IPO2SGHRerfz8svv2xdfPHFVlxcnDV48GBr9erVHe7NZVmW1bFjQAAAAOcGrtEBAADGIugAAABjEXQAAICxCDoAAMBYBB0AAGAsgg4AADAWQQcAABiLoAMAAIxF0AEAAMYi6AAAAGMRdAAAgLEIOgAAwFj/Dwj2rjwPewEzAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([HDvPop[t]/aDP for t in popHDlist],bins=40)\n",
    "plt.axvline(maxDistrictPop/aDP,ls=\"--\")\n",
    "print(\"current relative pop.  Total number of stillOver =\",len(stillOver))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "bb1a8873-c8a1-4c2d-be01-9cf363026d5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min, max uses are 0.806976733148653 1.303820314305861\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "print(\"min, max uses are\",np.min(unitUse),np.max(unitUse) )\n",
    "plt.hist(unitUse,weights=unitPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "145e1646-9fa6-44dc-8be5-d3832c91025b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(t,\"lost its home U\",homeU[t],\"with pop\",unitPop[homeU[t]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "5ef32a56-6b4a-4f51-8ff9-936963ae429c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, what if we drop overused units from these stillOvers until under, then adding others back in?\n",
      "of 191 overpopped, we will fix 191\n"
     ]
    }
   ],
   "source": [
    "print(\"OK, what if we drop overused units from these stillOvers until under, then adding others back in?\")\n",
    "#this is new of schoolsnap15\n",
    "fixableOverSet = set()\n",
    "barredOverUs = [list() for t in range(nHDs)]  #so we don't add them back in\n",
    "for t in stillOver:\n",
    "    adjoiners = getAdjoiners(HDunitList[t], unitNbrs)\n",
    "    edgeUset =  set(getAdjoiners(adjoiners, unitNbrs)).intersection(set(HDunitList[t])).difference({homeU[t]}) \n",
    "    edgeUs = list()\n",
    "    for u in edgeUset: #checking that we maintain contiguity dropping this u\n",
    "        unbroken, noEnclave, sPL, ePL = enclaveCheck(list( set(HDunitList[t]).difference({u}) ) ,unitNbrs)\n",
    "        if unbroken and noEnclave:\n",
    "            edgeUs.append(u)  #we only consider dropping a boundary unit that preserves contig'y\n",
    "    edgeUses = [-1.*unitUse[u] for u in edgeUs]\n",
    "    currPop, currList = np.sum([unitPop[u] for u in HDunitList[t] ] ), HDunitList[t].copy() #should be HDvPop[t], but to be sure...\n",
    "    jj, idxxx = -1, np.argsort(edgeUses)\n",
    "    while currPop > maxDistrictPop and jj + 1 < len(edgeUs) : #just drop (overused) units until no longer overpop\n",
    "        jj += 1\n",
    "        iNo = idxxx[jj]\n",
    "        tryU = edgeUs[iNo]\n",
    "        unbroken, noEnclave, sPL, ePL = enclaveCheck(list( set(currList).difference({tryU}) ) ,unitNbrs)\n",
    "        if unbroken and noEnclave:\n",
    "            currPop -= unitPop[tryU]\n",
    "            currList = list(set(currList).difference({tryU}) )\n",
    "            barredOverUs[t].append(tryU)\n",
    "    if currPop < maxDistrictPop : \n",
    "        for u in set(HDunitList[t]).difference(set(currList)):\n",
    "            unitUse[u] -= nDistricts * HDweight[t]\n",
    "        HDunitList[t] = currList.copy()\n",
    "        HDvPop[t] = np.sum([unitPop[u] for u in HDunitList[t] ])\n",
    "        fixableOverSet.add(t)\n",
    "print(\"of\",len(stillOver),\"overpopped, we will fix\",len(fixableOverSet) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "ad07dab7-3acf-4b40-a2eb-b721d950d2d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are now 191 HDs that are underpop\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "needToGrow = list()\n",
    "for t in fixableOverSet:\n",
    "    if abs(HDvPop[t] - aDP) > maxGap:\n",
    "        needToGrow.append(t)\n",
    "print(\"there are now\",len(needToGrow),\"HDs that are underpop\")\n",
    "plt.hist([HDvPop[t]/aDP for t in needToGrow], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "205d61d5-d5c9-4f31-9928-88731999f6d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188 191 fixed, total tried\n"
     ]
    }
   ],
   "source": [
    "fixableGrowSet = set()\n",
    "for t in needToGrow:\n",
    "\n",
    "    cantFix = True\n",
    "    \n",
    "    contigUs, contigPop = HDunitList[t].copy(), HDvPop[t] #list(set(HDunitList[t]).difference( {tryU} ) ), HDvPop[t] - unitPop[tryU]\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjSet, adjoiners =  set( getAdjoiners(currList, unitNbrs) ).difference(set(barredOverUs[t])) , list()\n",
    "    for uu in adjSet:\n",
    "        if contigPop + unitPop[uu] < maxDistrictPop:\n",
    "            adjoiners.append(uu)\n",
    "    nearHDlist = [uu for uu in adjoiners]  \n",
    "    nearHDscore = [ unitUse[uu]  for uu in adjoiners ]  #simpler scoring system, only adding a few\n",
    "    stillGoing = True\n",
    "\n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing and cantFix:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            contigPop += unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]: # ... and add its nonHD, nonwhole neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap and uu not in {tryU}:  \n",
    "                    nearHDlist.append(uu)                           #subbed HDcircle for HDpoly in below\n",
    "                    nearHDscore.append(1.1 * unitUse[u]) #1.1 to encourage original neighbor pickup \n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "\n",
    "        if abs(gap) < maxGap :\n",
    "            #print(\"I fixed stillUnder\",t,\"its final pop - aDP is\",int(contigPop - aDP))\n",
    "            cantFix = False\n",
    "            fixableGrowSet.add(t)\n",
    "            for u in addedList:\n",
    "                unitUse[u] += HDweight[t] * nDistricts\n",
    "            for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "                unitUse[u] -= HDweight[t] * nDistricts       \n",
    "            HDunitList[t] = contigUs + addedList\n",
    "            HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "            break\n",
    "\n",
    "print(len(fixableGrowSet), len(needToGrow),\"fixed, total tried\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12192a10-cd2d-4a08-8ef5-468827329a01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "id": "a7707d58-ac73-4507-8fc9-2b5a9b447a80",
   "metadata": {},
   "source": [
    "print(\"these areas are highly blocky.  Look to pair two or three units where possible\")\n",
    "blockyList, failedList = list(), list()\n",
    "for t in stillUnder + stillOver:\n",
    "    hU = homeU[t]\n",
    "    newPop, newList = unitPop[hU], [homeU]\n",
    "    isSolved = False\n",
    "    for u in unitNbrs[homeU[t]]:\n",
    "        if abs(newPop + unitPop[u] - aDP) < maxGap:\n",
    "            newPop += unitPop[u]\n",
    "            newList = [hU,u]\n",
    "            isSolved = True\n",
    "            break\n",
    "    if abs(newPop - aDP) > maxGap:\n",
    "        for u in unitNbrs[hU]:  #try 3-way\n",
    "            trialPop = newPop + unitPop[u]\n",
    "            eitherNbrs = list( set(unitNbrs[u] + unitNbrs[hU]).difference({hU,u}) )\n",
    "            eitherDist = [unitCP[uu].distance(HDCP[t]) for uu in eitherNbrs]\n",
    "            nEither = len(eitherNbrs)\n",
    "            idx = np.argsort(eitherDist)\n",
    "            for i in range(nEither):\n",
    "                iNo = idx[i]\n",
    "                if abs(trialPop + unitPop[eitherNbrs[iNo]] - aDP) < maxGap:\n",
    "                    isSolved = True\n",
    "                    newList = [hU, u, eitherNbrs[iNo] ]\n",
    "                    break\n",
    "    if isSolved:\n",
    "        unbroken, noEnclave, __, ___ = enclaveCheck(newList,unitNbrs)\n",
    "        if unbroken and noEnclave:\n",
    "            HDunitList[t] = newList.copy()\n",
    "            HDvPop[t] = np.sum([unitPop[u] for u in newList])  #don't worry about unitUse updates; will do later\n",
    "            blockyList.append(t)\n",
    "        else:\n",
    "            isSolved = False\n",
    "    if not isSolved:\n",
    "        failedList.append(t)\n",
    "print(\"all done trying to build 2- and 3-unit districts.  nSuccess and nFail were\",len(blockyList),len(failedList))\n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "bfd8b2e1-09ce-43b1-b5ec-84b134d76026",
   "metadata": {},
   "source": [
    "print(\"before resorting to splitting units, try once more with MustSpawn\")\n",
    "\n",
    "#THIS IS THE MUSTSPAWN code block - stolen from vanillaHD-OHredo\n",
    "print(\"This takes 1 - 1000 sec per HD :-( The total number of HDs to fix is\",len(badDiscoList))\n",
    "#avgDiam = MAP.area**0.5 / float(nDistricts)\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(failedList):\n",
    "    if i%10 == 0:\n",
    "        print(\"swell-generating HD\",t,\"our\",i+1,\"th HD we must regrow out of\",len(failedList),int(time.time()-startTime),\"sec elapsed\" )\n",
    "    notInCluster = True\n",
    "    for jj, geo in enumerate(CCBgeom):  #swell in-corner HDs from the corner cluster\n",
    "        if geo.contains(hdCP[t]):\n",
    "            starterU = allUnits.index(jj+0.25)\n",
    "            notInCluster = False\n",
    "    if notInCluster:\n",
    "        starterU = homeU[t]\n",
    "        #distList = [hdCP[t].distance(unitCP[u]) for u in HDunitList[t] ]\n",
    "        #starterU = HDunitList[t][distList.index(np.min(distList))]  #starter = unit with centroid closest to the hd center\n",
    "    contigUs = [starterU]  #we used getContigFromStarter(starterU, HDvtdList[t], unitNbrs) in above code block\n",
    "    contigPop = np.sum( [ unitPop[u] for u in contigUs ] )\n",
    "    gap = aDP - contigPop\n",
    "    origGap = gap\n",
    "    currList, addedList = contigUs.copy(), list()\n",
    "    adjoiners = list( set( getAdjoiners(currList, unitNbrs) ).difference(wholeSet) )\n",
    "    nearHDlist = [uu for uu in adjoiners]  #bias toward underused, close to HD (and its center)\n",
    "    #                                            subbed HDcircle for HDpoly in below\n",
    "    nearHDscore = [ (0.2*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDcircle[t]) + \n",
    "        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] for uu in adjoiners ]\n",
    "    stillGoing = True\n",
    "    \n",
    "    while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "        idx, i, notYetPicked = np.argsort(nearHDscore), 0, True\n",
    "        while i < len(nearHDscore) and notYetPicked:        \n",
    "            listNo = idx[i]   #nearHDscore.index(np.min(nearHDscore))\n",
    "            unitNoToAdd = nearHDlist[listNo]  #add this unit ...\n",
    "            canAdd  = wontEnclave(unitNoToAdd, currList, unitNbrs, borderUnits)\n",
    "            if canAdd:\n",
    "                notYetPicked = False\n",
    "            else:\n",
    "                i +=1\n",
    "        if notYetPicked:\n",
    "            stillGoing = False  #can't add any more units without creating an enclave\n",
    "        else: #we selected the best unit to add legally\n",
    "            gap -= unitPop[unitNoToAdd]\n",
    "            addedList.append(unitNoToAdd)\n",
    "            currList.append( unitNoToAdd)\n",
    "            for uu in unitNbrs[unitNoToAdd]: # ... and add its nonHD, nonwhole neighbors to future candidates\n",
    "                if uu not in currList and uu not in nearHDlist and unitPop[uu] < gap + maxGap and uu not in wholeSet:  \n",
    "                    nearHDlist.append(uu)                           #subbed HDcircle for HDpoly in below\n",
    "                    nearHDscore.append((0.1*(unitUse[uu] - 1.) ) + 0.5*(unitCP[uu].distance(HDcircle[t]) + \n",
    "                                                                        unitCP[uu].distance(hdCP[t])  )  / HDdiam[t] )\n",
    "            del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "            del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "            for i, uu in enumerate(nearHDlist.copy()):\n",
    "                if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                    del nearHDscore[nearHDlist.index(uu)]\n",
    "                    del nearHDlist[ nearHDlist.index(uu)]\n",
    "    for u in addedList:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    for u in list( set(HDunitList[t]).difference(set(contigUs)) ):\n",
    "        unitUse[u] -= HDweight[t] * nDistricts       \n",
    "    HDunitList[t] = contigUs + addedList\n",
    "    HDvPop[t]    = np.sum( [unitPop[u] for u in HDunitList[t] ] )\n",
    "    HDnAddedUnits[t] = len(addedList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "25d2956a-4ed4-4d94-abec-71bf5c127e0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updated pop histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"updated pop histogram\")\n",
    "plt.hist([HDvPop[t]/aDP for t in popHDlist], bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "8bfe6602-0b90-40ee-b7ed-4480fb1cb4fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3781 0.99316\n",
      "4834 0.99356\n",
      "4987 0.99274\n",
      "OK, only three off\n"
     ]
    }
   ],
   "source": [
    "stillOff = list()\n",
    "for t in popHDlist:\n",
    "    if abs(HDvPop[t] - aDP) > maxGap:\n",
    "        print(t,r5(HDvPop[t]/aDP))\n",
    "        stillOff.append(t)\n",
    "print(\"OK, only three off\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "1176b3ff-e59d-4718-ac42-7307e3c5b4c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simply assign the nearest successful t's unit List\n",
      "forcing HDunitList for 3781 to match that of 4628 , which are both in home unit 549 with pop 5000\n",
      "forcing HDunitList for 4834 to match that of 5108 , which are both in home unit 535 with pop 5542\n",
      "forcing HDunitList for 4987 to match that of 4278 , which are both in home unit 535 with pop 5542\n"
     ]
    }
   ],
   "source": [
    "print(\"simply assign the nearest successful t's unit List\")\n",
    "for t in stillOff:\n",
    "    u = tractMuniNo[t]\n",
    "    nearbyTs = list( set(muniTractList[u]).difference(set(stillOff)) )\n",
    "    if len(nearbyTs) > 0:\n",
    "        dists = [tractCP[tt].distance(tractCP[t]) for tt in nearbyTs]\n",
    "        tt = nearbyTs[dists.index(np.min(dists))]\n",
    "        HDunitList[t] = HDunitList[tt].copy()\n",
    "        HDvPop[t] = HDvPop[tt]\n",
    "        print(\"forcing HDunitList for\",t,\"to match that of\",tt,\", which are both in home unit\",u,\"with pop\",unitPop[u])\n",
    "    else:\n",
    "        print(\"Bummer.  No other eligible HDs that share the same home unit as stillOff\",t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "269b3356-65cd-43f0-b960-a0a97f782e93",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "update classification of HDs by contiguity\n",
      "quick classification: who has contiguity problems?\n",
      "working on HD 0 time is now 0\n",
      "working on HD 700 time is now 1\n",
      "working on HD 1402 time is now 2\n",
      "working on HD 2102 time is now 3\n",
      "working on HD 2802 time is now 5\n",
      "working on HD 3503 time is now 6\n",
      "working on HD 4204 time is now 7\n",
      "working on HD 4906 time is now 8\n",
      "working on HD 5606 time is now 9\n",
      "working on HD 6306 time is now 10\n",
      "working on HD 7006 time is now 12\n",
      "working on HD 7706 time is now 13\n",
      "working on HD 8407 time is now 14\n",
      "out of 8934 total HDs, there were 8934.0 contiguous and 8934.0 complement-contiguous HDs\n",
      "0 HDs had both discontiguity problems, while enclave-only = 0 and discontig only= 0\n"
     ]
    }
   ],
   "source": [
    "print(\"update classification of HDs by contiguity\") #take from vanillaHD\n",
    "\n",
    "### THIS IS THE \"FIND DISCO\" CODE\n",
    "print(\"quick classification: who has contiguity problems?\")\n",
    "nUnbroken, nNoEnclave, smallPieceLists, enclaveLists, sPgenerator, eLgenerator = 0., 0., list(), list(), list(), list()\n",
    "smallPieceLengths, enclaveLengths = list(), list()\n",
    "doubleTroubleList = list()\n",
    "startTime = time.time()\n",
    "for i,t in enumerate(popHDlist):\n",
    "    if i%700 == 0:\n",
    "        print(\"working on HD\",t,\"time is now\",int(time.time() - startTime) )\n",
    "    unbroken, noEnclave,smallPieceList,enclaveList = enclaveCheck(HDunitList[t], unitNbrs,4) #,6) #6 is high (slow) to try to avoid false enclaves\n",
    "    if unbroken:\n",
    "        nUnbroken +=1\n",
    "    if noEnclave:\n",
    "        nNoEnclave +=1\n",
    "    if not unbroken:\n",
    "        smallPieceLists.append(smallPieceList)\n",
    "        smallPieceLengths.append(len(smallPieceList))\n",
    "        sPgenerator.append(t)\n",
    "    if not noEnclave and unbroken:   #district is contiguous but contains 1+ enclave\n",
    "        enclaveLists.append(enclaveList)\n",
    "        enclaveLengths.append(len(enclaveList))\n",
    "        eLgenerator.append(t)\n",
    "    if not noEnclave and not unbroken:  #extremely discontig (\"broken\") HDs can appear to have enclaves;\n",
    "        doubleTroubleList.append(t)\n",
    "print(\"out of\",len(popHDlist),\"total HDs, there were\",nUnbroken,\"contiguous and\",nNoEnclave,\"complement-contiguous HDs\")\n",
    "print(len(doubleTroubleList),\"HDs had both discontiguity problems, while enclave-only =\",len(eLgenerator),\n",
    "      \"and discontig only=\",len(sPgenerator)-len(doubleTroubleList) )\n",
    "\n",
    "if len(smallPieceLists) + len(enclaveLists) > 0:\n",
    "    print(\"here are the histograms of the small piece and enclave list lengths, total no =\",\n",
    "          len(smallPieceLists),len(enclaveLists) )\n",
    "    plt.hist([s for s in smallPieceLengths],label=\"small piece l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.hist([e for e in enclaveLengths],label=\"enclave l units\",histtype='step',\n",
    "             cumulative=True,bins=[0,1,2,3,4,5,6,8,10,12,15,20,25,30,35,40,45,50,60,80,120,200])\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    print(\"And here are the small-HD-piece and small-enclave pops\")\n",
    "    plt.hist([sum(unitPop[u] for u in s) for s in smallPieceLists],label=\"small piece 1 pop\",histtype='step')\n",
    "    plt.hist([sum(unitPop[u] for u in e) for e in enclaveLists],label=\"enclave 1 pop\",histtype='step')\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58c9778a-19da-473c-85ec-34139b0531ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "ec95e007-0d57-4391-a110-177c20f5fda6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating our unitUse, underpopped and overpopped lists\n",
      "unit use histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unit use by pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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1IXcEDrf7LwAAvZHjgxF/QHCkpeezIv6FFStNz1p9RkvW1ITciI9EWACAkzg6GOkqIOiOfHeWll8/JWh5pdVnVLHrkF6p2qeKXYcCwUVlbUOnGZH2SIQFADiFo3NGIgUEVt1YcoYuHV/QKfG0q3yQlpM+S+dm918AQG/n6JmRWD3oLx1foJIxgzsFIl3lg+w+2GTp3Oz+CwDo7RwdjPT0QR8uWdVKPsjKyj3Kzwm/a3A0ibAAAKQyRwcj0wpzVeAOHxBY0TFZtdVn9OybtRHzQeq9Lbpm2ihJ6nT9aBJhAQBIdY4ORtLTXFpcVtSt9w7q17dTsmp5dZ0uevg1Pbh2h6VzjB7Sr23XYHfwDE2oRFgAAHorRyewSm279i6/foru+917OnzshOX3fXY0+Fh/jkg0lTnDBmSpZMxgTS/KpwMrAMCxHB+MSG0ByYDMvrrul5ujet+SNTWaXpQf+G+rgYhLbbMf/nyQ9DSXSsYMjuraAAD0FgQjn/tiYa5y+/dVQ5P12ZH2fUCslgiTDwIAQDCCEZ3qBxJNIOJX7zmmtCiCinz2nQEAIIjjg5Hu5Hq09+Jf/6F/Lz3L0rGLZn1B/3JhITMiAAC04+hqmli0g3+79pAmjRzYZYmwv2cIgQgAAJ05OhiJRTt4I+n5zR8HSoTpGQIAQHQcHYzEqh38xw1HAyXC9AwBACA6js4Zsbo/TCRn5PaT1FYiTM8QAACi49hgpNVntLJyT4/P43JJN5SMDnxPzxAAAKLj2GWaytoG1XtbenyeWy8qVEYfx95GAAB6zLFP0Vjki0wvGqbvzere3jYAAKCNY5dpdh040u33pkn6r29O1v+dNDx2AwIAwKEcOTPS6jN65q3d3X7/F4YP0KDTMtTq60mHEgAAIDk0GKmsbVBj88luv//9Txp13S82a+r316m8ui6GIwMAwHkcGYzEqr/I4aMnNHvFNgISAAB6wJHByLABWZEPisJ9v3tPb354kGUbAAC6wZHByLTCXBW4YxeQHD52Qtf9crMuevg1ZkkAAIhS1MHIxo0bVVZWpuHDh8vlcmn16tVdHl9XV6drr71WZ599ttLS0nTnnXd2c6ixk57m0qI4lOTWeZo1h2UbAACiEnUw0tTUpIkTJ2rZsmWWjm9padHQoUO1cOFCTZw4MeoBxsug/hlxOa+RtGRNDUs2AABYFHWfkUsvvVSXXnqp5eNHjx6txx9/XJL09NNPR3u5uIlVEmsodZ5mVdY20BYeAAALkrLpWUtLi1paTrVq93q9Mb9GrJNYO/rj50s1bJQHAEDXkjKBdenSpXK73YGvkSNHxvwa0wpzlRXHPWWeq/hY1zz1NkmtAABEkJTByIIFC+TxeAJfe/fujct1jIl/Xkc9Sa0AAHQpKZdpMjMzlZmZGddrVNY2qKU1/sGI/wp3//YdvbfPoy+dOUQXjBnM0g0AAJ9LymAkEeKZwBpKU0urlr2+S8te36WB/frqoasmaOb4goSOAQCAZBR1MHLkyBHt3Lkz8H1tba2qqqqUm5urUaNGacGCBdq3b5+ee+65wDFVVVWB93766aeqqqpSRkaGiopi3+vDqt0Hj9p2bX8b+SevnxK3gKTVZ1RZ26ADjc0aNiCLRFoAQNJymSgTJzZs2KCLL7640+s33XSTnn32Wf3Lv/yLdu/erQ0bNpy6iKvzQ/CMM87Q7t27LV3T6/XK7XbL4/EoJycnmuGG1OozuvCh11TvTezsSEf5OZl6875LYh4klFfXacmaGtV5Tv1+Be4sLS4rYjYGAJAwVp/fUQcjdoh1MFKx65CueertGIys51bedkFM+5GUV9dpzopt6vg/1R/uLI/jbAwAAO1ZfX4nZTVNvCU6X6QrsRxLq89oyZqaToGIdCqRlu6wAIBk48hgJN4Nz6IRy7FU1jYELc10ZHSqOywAAMnCkcFIrHft7a78nExNK8yN2fmszrIk08wQAACODEbS01xaXFYku2tLLp80PKbJq1ZnWZJpZggAAEcGI5I0c3yB7iw929YxvLjlHzHN3/DP+IQLb1xqq6qJ5WwMAAA95dhgRJI8x47bev3Pjp7QWzsPqmLXIb1StU8Vuw71KDjxz/hI6hSQ+L9fXFZEvxEAQFJxbAfWVp/R6qpP4nJulxSyoiWU2b/ZqqaW1sD3Pe0HMnN8gZZfP6VTn5F8+owAAJKUI/uMSPHtNVLgztKkkW79sXp/1O+NVT8QOrACAOxm9fnt2JmReFWUXDFpuP558umqqfd2KxgxagtIlqyp0fSi/G4HEOlprpg2UwMAIF4cG4zEq6JkddUnPV7+ad8PhIACANDbOTaBdeoZg+weQkT0AwEAOIFjZ0a2fvyZ3UOI6GBji16p2pfwnA/yTQAAieTYYCTZZx3SXNKDa3cEvk/Urrvs+AsASDTHLtMkexfSju1G6j3NmrNim8qr6+J2Tf+Ovx33t0nEtQEAzuXYYGRaYa4GZKXbPQzL4r3rLjv+AgDs4thgJD3Npamj7E1iLXBn6YlrJ+sui23p47nrLjv+AgDs4thgRJIuHDvE1usvmlWkGeMLtOqve6J6XzzyXdjxFwBgF8cmsErS2cMG2HZtl6QH19bI3a9vlzMSocQj34UdfwEAdnHszEh5dZ3ueKHKtuv7lz0qdh2y/J547rrLjr8AALs4MhjxV40cPnbC7qHI+pZ6bUfGa9dddvwFANjFccFIV1Ujdojm4T6wX19NL8qP21j8O/7mu4OXYvLdWT3euA8AgHAclzMSqWokkQrcWXrhr3stH3/46Im471czc3yBphfl04EVAJAwjpsZSaZqkG9+cZTqvS1RvWddTX2cRnOKf8ffyyeNUMmYwQQiAIC4clwwkizVIO7sPvIeOx71+16p+oTGYwCAXsVxwYi/asRunmMn9cs3d0f9vkNNx2k8BjhEq8+oYtchvVK1TxW7DvFBBL2W43JG/FUjs1dss3sokto2xDMmmpqa5FpqAhAfbFoJJ3HczIjUlqQ549w8u4chqW1DvGg/6ww5LTMuYwGQHNi0Ek7jyGBEkm68YLTdQwjolxHdhn13v1jFHyOgl2LTSjiRY4ORhqPRJ4/Gy9HjrVEdv9/bwqcjoJdi00o4kSODkfLqOn175Xa7h9FtfDoCei82rYQTOS4Y8U+Bpjo+HQG9E5tWwokcV02TTB1YY6Hjp6NWn6F7KpDC/O0H6j3NIfNGXGrbooFNK9GbOC4Y6W1Tm+0/HVEKCKQ+f/uBOSu2yaXgajs2rURv5bhlmlSa2uzqb41LbYGG/9MRpYBA78GmlXAax82MTCvMVX5Oluq9yTlDkpnu0q1fOVNfOnOIPMeOa+7zbYm2XX06ilQK6FJbsuv0onw+TQEpgk0r4SSOmxlJT3PpG+efbvcwwmppNVr2+i59e9V2bfn4M91ZepbycoKbnHX8dEQpINA7sWklnMJxMyOStLfhqN1DiKih6bie/nzvmvycLN1VerZGD+kX8tMRpYAAgFTmuJkRSWqKssmY3eq9zXrsz39XZp+0kJ+OKAUEAKQyRwYjAzKja7+eLMI1OfOXAoabwO2Y7AoAQDJxXDDS6jP6y4ef2j2MqHWV9+EvBZTUKSChFBAAkOwcF4xU1jbowJETdg+j28LlfVAKCABIVY5LYE31JM6u8j4oBQQApCLHBSOpmsRptQW0vxQQAIBU4bhlmramZ5mRD0wi5H0AAHozxwUj6WkuPfD1c+0eRlgXnzNEuf37Br1G3gcAoDdz3DKN1JZb8cS1k/Vvn7dat0OaSwpRpau/1R/R9y+foEH9M8j7AAA4guNmRvxera639fqhAhGpbWO7uc9vk+fYcVpAAwAcwZHByKvv1ukP7ybnLrb+GCVcgzMAAHobxwUjrT6jha9U2z2MLrGxHQDASRwXjFTWNqih6bjdw7Ak1XuiAABgheOCkVR6wB9sbGGpBgDQ6zkuGEmlpmcPrt2hix5+TeXVyZnfAgBALDguGJk0cqDdQ4hKvadZc1ZsS7qApNVnVLHrkF6p2qeKXYeYwQEAdJvj+ow8v/lju4cQFaO2DqxL1tRoelF+UpT5llfXacmaGtV5Ti15FbiztLisiMZsAICoOW5m5OOGo3YPIWrJVF1TXl2nOSu2BQUiUvLO4AAAkp/jgpGjLSftHkK32Z182+ozWrKmRqEWZOiPAgDoLkcFI60+o007D9o9jG6zO/m2srah04xIe8k0gwMASB2OyhmprG1QvbfF7mFEzaW2zfKmFebaOg6rMzN2z+AAAFKLo2ZGUvEh6U9XXVxWZHvyqtWZGbtncAAAqcVRwciQ/pl2DyFq+e4sLb9+SlJUqUwrzFWBO0vhQiKX2qpq7J7BAQCklqiDkY0bN6qsrEzDhw+Xy+XS6tWrI75nw4YNmjJlijIzMzV27Fg9++yz3RhqDNhfFdsl1+dfd5Werce/OUkrb7tAm+79WlAgYmd/j/Q0lxaXFQXG2nHsUnLM4AAAUkvUOSNNTU2aOHGibrnlFl111VURj6+trdWsWbM0e/Zs/eY3v9H69et16623qqCgQDNmzOjWoLvr4JHkzhfJj9CrI9r+Hq0+o8raBh1obNawAW0zFj0NFGaOL9Dy66d0GkeksQMAEI7LGNPtj9Yul0svv/yyrrjiirDH3HvvvVq7dq2qq0/tlPvNb35Thw8fVnl5uaXreL1eud1ueTwe5eTkdHe4qth1SNc89Xa33x9P8y4eo7umnxM2WPD39+j4P8t/dMelnHg3JotHoAMA6F2sPr/jnjNSUVGh0tLSoNdmzJihioqKsO9paWmR1+sN+ooFf85DMrpw7NCwD/No+3skojFZeppLJWMG6/JJI1QyZjCBCACg2+IejNTX1ysvLy/otby8PHm9Xh07dizke5YuXSq32x34GjlyZEzGkp7m0v89L/mWEQZm9w1K+uyYF/L2rkOW+3vQmAwAkGqSss/IggULNH/+/MD3Xq83JgFJq8/od9v29fg8sXbzhaMDMwuhllcGZve1dJ4Djc1RNSYrGTO4R+MGACAW4h6M5Ofna//+/UGv7d+/Xzk5OcrOzg75nszMTGVmxr4Mt7K2QQ1Nx2N+3p4Y1K+v5n3tLEnh80IOHzth6VzDBmTRmAwAkHLivkxTUlKi9evXB722bt06lZSUxPvSnSTjA/g/r5ig9DRXl8srkbTv70FjMgBAqok6GDly5IiqqqpUVVUlqa10t6qqSnv27JHUtsRy4403Bo6fPXu2PvroI91zzz3629/+pieeeEIvvvii7rrrrtj8BlFIxgfwoP4ZkiLv+xJOx/4eNCYDAKSaqIORLVu2aPLkyZo8ebIkaf78+Zo8ebLuv/9+SVJdXV0gMJGkwsJCrV27VuvWrdPEiRP14x//WL/4xS8S3mNEaqumye1vLf8iUdbV1EuyPmvTMX+kY4dWGpMBAFJNj/qMJEqs+oxI0pLfv69n3todm4HFwOD+Gar8Xqkqaxss9UC585KxKj5zSMT+HvHuMwIAQCRWn99JWU0TT6cPCp00a5dDTcdVWdugaYW5ys/JUr236xmSX721W2cMOU35OV03Gps5vkDTi/IT0piMBmgAgJ5wXDCS+3mORjI50Nis9DSXzh89SH94t+uGZJ8dO6m7XqiSFHmmw9+YLJ6YgQEA9JSjdu2VpHx3cs2MSG2Jta0+o00fHozqfbHsqNodiej0CgDo/RwXjHyWRH1G2le2VNY2WO4n4mdnR1U6vQIAYsVRwUirz+g//lBj9zCC+CtbutsDpX1H1USKptMrAABdcVQwUlnbEDFBNFHSXNKyaycH8ip62gMl0Q3d6PQKAIgVRyWwJtOD0WekQf1Ptbz3Nyur9zR3qwtrohu6RdPplWobAEBXHBWMJFsH1nrPqV2L/c3K5qzYFvV57OioGil4cqmtIdtnTcd10cOvUW0DAAjLUcs0bR1Yk6e098G1O4IqTmaOL9Dy66eowB1d0LRoVuI7qlrp9Pr1iQWa+zzVNgCArjkqGElPc+mKScPtHkbAZ03HOz2UZ44v0BvfuViLZn1BE0+31m12kE0Blj94yu8QPOW7s7Ts2sn6/Tt1VNsAACJy1DKNJE0vytfTb+62exiS2h7KLrU9lKcX5Ss9zRWyiVgkdubChOv0Gk21TbwbswEAkpvjgpFphbkamN036p4e8dL+oew51jZTEu1cgd25MKE6vVJtAwCwylHLNFLbg/PmCwsTcq1osjjqPcfCNhHr6vx2JK9aEU21DQDA2RwXjEjSvK+N1WmZ6XE596B+ffXk9VP0ZIhciq40NB2PamnGH+j4m6YlG3+1TbiRJXMgBQBILEcGI+lpLl19/siYnvOKScP1m1uLtWXhdE0vypc7O0P3zDhH37tsnNzZfcO+z/9Qzj0tM+wxobiz++jO0rPUctKnil2Hki4R1Eq1TbIGUgCAxHJczojfgKzwAUJ3XDxumC4cOyRkAurAfm3XcklByzDtH8ru7OgqYrzNJ/WTP38Y+D4Ze3f4q2063o/8JBwrAMA+jgxGWn1Gv6rYHdNzDhuQFdjFtuMchedoW7Ksu19fHT56KnG2/UO51Wei6sDacSLE37tj+fVTkuohH67ahhkRAICfI4ORytoGfXY0NtU0/k6jU88YpK/+6PWwfTVckrL6pOk3txbr4JGWTg/lnnRgbX+N9mXC7dnZkj1UtQ0AAH6ODEZiXU66aFaRfl2xO2JfjXpvi9JcLl0+aUTIY/zLGt99uVoNTcejHke43h2hlo6ScVkHAOBMjkxgjWU56WUT8vXg2ho9uHaHpePrvc2q2HVIr1TtC5l4OnN8gd5ecIly+3c/p6V9sOVfOqIlOwAgWTlyZmRaYa6y+qSp+aSvx+da+159VMc/+If31dB0aoko1AxFRp80/eDKCYElm+42QWv1mbC9SyIt6wAAkCiOnBlJT3Pp/5wz1JZrtw9EpPAzFOH2fekqZujYuyOaluwAANjFkTMjknRDyWiVv7/f7mF0OUMRqhLls6bjmvt85xmTUL07aMkOAEgFjg1GLjhzsDLTXWpptb9ZWFebxoWqRFmeZq13By3ZAQCpwLHBSHqaS5d8IU+vVkeX8xFPVmcorPbu8LdkD9e7xF+WTEt2AICdHBuMlFfXJVUgInU9QxGqT0ik3h3te5d01f2V5FUAgJ0cGYy0+owe+H2N3cMIiDRD0ZM+IbRkBwAkO0cGI5W1Dar3JkfSZqQZinAt5qNp/05LdgBAMnNkMJIsgYjU9QxFLPuE0JIdAJCsHBmMNBxpsXsIkqR5F4/VXdPPDhtIRNMnhEADAJCqHNn0LLd/ht1DkCRdOHZIlzMaVqtr3tz5aae28gAApApHBiPDcuzvqzGwX9+IJbVW+3/87PVduujh19hnBgCQkhwZjES92UscHD56Qutqui4t9vcJsZJmysZ3AIBU5chg5GBTYnJG+memh/2ZP/m0q+UVf58Q//Fd8Z8l0jkBAEg2jgxGhvTPTMh1mlpaw/7M6iZ104vydWfpWXJn9414PTa+AwCkIkdW01ha90iQrpJUQzU76+k5uyNU91d6lAAAYsWRwcjBOJf2utRWsXOo6XjEY8MlqYZrdmZFLDe+60n3VwAArHDkMk0sH9Yd5wf83z94+fguk09danuoh6qo6arZWaSxFLizNPWMQarYdUivVO1Txa5D3c4h8QdEHWdmkjVZttVnYvJ7AwASy5EzI9MKc5Wfk6l6b/dnSAZm99UPrpygB9eG3/MlLU3d2qQuUrOzUPxn+frEAn31R6/3eCYjlt1fE4EZHABIXY4MRtLTXLpm2ij95M8fdvscN184WpedV6AZ48Pv+dLdTeq6k/OR787S1ycW6Ocba3u0j41fKnV/jcX+PQAA+zgyGJGk0UP69+j9h48eV8WuQ5pWmNvlw7g7m9RZXUZaNOsLGjIgU8MGtC3NfPVHr8dsJsNqQBTrZNlopdoMDgCgM8cGIz3NG3nmrY/1zFsfW1oKiHaTOn+zs3pPc8iHrEttMyH/cmFh4AFbsetQTGcyrN6fWObfdEcqzeAAAEJzZAKr1PbAj8UeNfFI5uyq2Vm4fJNYz2RE6v7aVQJuIqXKDA4AIDzHBiPpaS5dMWl4j88Tr86n/nyTfHfwzEO+OysoB8JfQfLh/kZL57U6k9GdgMgOqTKDAwAIz7HLNJJ0ybg8Pf3m7h6fJx5LAa0+I3d2hu6ZcY4amo4r97RM5ecE55tE0xTNv7QTzUxGdxNwE8nqkpbdMzgAgPAcG4yUV9fpgd/XxPScf66pj0kw0lWZavtAxGpTtJ7MZHQnATeR/DM43SmhBgAkB5cxJuk7Q3m9Xrndbnk8HuXk5PT4fD3pbhrJkz0sIw03Nv+jdPn1UzS9KF8XPfya5V4kTui3QZ8RAEg+Vp/fjpsZ6W53U6t6UkZqtUx1QGZfS4HIvIvH6sKxQ5JqJiNekn0GBwAQnuOCke50N41GT3JHrJapVnx00NL5zso7zVHlrNGWUAMAkoPjqmkSUeL55s6DavWZqPdKsT42a5/2qSABAKQCx82MJOIB/bPXd2rF5o8lSYePngi8HimHwerYSsYM1u+2/YMKEgBAr+C4mRF/KWi8HT56IigQkSI3SLPaaOyCMwenRA8QAACscFww0r6ZV6JFapAWTaMxq03RAABIdo4s7ZWkV9+t07yV2xTDpqlRWXnbBWGTLaMpU231GSpIAABJidLeCC47r0Df3j9Wj6/facv1u0pWjaZMlQoSAECqc2wwIklnDj3NtmtHSlYlyAAAOIWjg5Eh/TMTfk0qXQAACOboYMRiu46YX45Kl8QgnwYAUoOjg5GDR1ridu6B/fpKCu4zkky73fZ27FUDAKnD0cFIPBqgXX/BKM2aMDywDMMn88QLt9mgv88Lpc8AkFwcHYxMK8zVwH59OzUn64lZE4YHJZ7GOwmVpYhgVjcb7O5mhgCA2OtW07Nly5Zp9OjRysrKUnFxsSorK8Mee+LECf3Hf/yHxowZo6ysLE2cOFHl5eXdHnAyK0hwYmp5dZ0uevg1XfPU27pjVZWueeptTX1wnR7/84cR98HpraxuNlhZ25C4QUUh2v2MAKAnkuVvTtQzIy+88ILmz5+vJ598UsXFxXrsscc0Y8YMffDBBxo2bFin4xcuXKgVK1boqaee0rhx4/SnP/1JV155pd566y1Nnjw5Jr9Ed1XWNsR0VmTRrC8k7NN2uKWIw8dO6Cd//rueeatWD101wXHLEVY3G0zEhonRIs8FQCIl09+cqGdGHn30Ud122226+eabVVRUpCeffFL9+vXT008/HfL4X//61/rud7+ryy67TGeeeabmzJmjyy67TD/+8Y/DXqOlpUVerzfoKx5i/UAalKBS4a6WIvwOHz2h2V3sg9NbWc0DSrYdjf3BZcdZnUj7GQFAdyTb35yogpHjx49r69atKi0tPXWCtDSVlpaqoqIi5HtaWlqUlRX8hz87O1ubNm0Ke52lS5fK7XYHvkaOHBnNMC2L9QMpUZ+2Iy1FtBduH5zeyupmg8nU5yVSnovkvP+PAOInGf/mRBWMHDx4UK2trcrLywt6PS8vT/X19SHfM2PGDD366KP68MMP5fP5tG7dOr300kuqqwsfdS1YsEAejyfwtXfv3miGaZk/gTVWEvVpO5qgJ5nzI+Ihms0Gk0Wq57kASC3J+Dcn7rv2Pv744zrrrLM0btw4ZWRkaN68ebr55puVlhb+0pmZmcrJyQn6iod1NfUxyxlJ5KftaIOeZMyPiKdU29E4lfNcAKSeZPybE1UC65AhQ5Senq79+/cHvb5//37l5+eHfM/QoUO1evVqNTc369ChQxo+fLjuu+8+nXnmmd0fdQz4p6liJZGftv1LEVaXapItPyIRotls0G6pmucCIDUl49+cqGZGMjIyNHXqVK1fvz7wms/n0/r161VSUtLle7OysjRixAidPHlSv/vd73T55Zd3b8QxEk3eRST/euHohH7a9i9FRHqsJmN+RCL5Nxu8fNIIlYwZnJSBiJSaeS4AUlcy/s2Jeplm/vz5euqpp/SrX/1KO3bs0Jw5c9TU1KSbb75ZknTjjTdqwYIFgeM3b96sl156SR999JH+8pe/aObMmfL5fLrnnnti91t0Qyynn0qLQs8KxZN/KSJczkuy5kegs1TMcwGQupLxb07UwcjVV1+tRx55RPfff78mTZqkqqoqlZeXB5Ja9+zZE5Sc2tzcrIULF6qoqEhXXnmlRowYoU2bNmngwIEx+yW6IxbTT3Z/Yp05vkBbF07XXaVnaWB2cFCSrPkRCC3V8lwApLZk+5vjMsYkfb2g1+uV2+2Wx+OJWTLrq+9+on97fnu33++PF5PlQUFb+N6B/48AEinef3OsPr8duTdNq8/owbU7enSOZNuB158fgdTG/0cAiZQsf3PiXtqbjGKRvLpo1heSJhABACCVOTIY6WnyqkvSg2t30BETAIAYcGQwsvtgU4/eT0dMALGSLLumAnZyXM5Iq89oZeWemJyLjpgAeiKZdk0F7OS4mZHK2gbVe1tici46YgLormTbNRWwk+OCkVjMZtjdXwRAakvGXVMBOzkuGInVbAYdMQF0VzLumgrYyXHBSKSe/JG4JC27djLruQC6LRl3TQXs5LhgpKue/FYYSYP6Z8Z0TACcJRl3TQXs5LhgRArfk98qPq0A6Ilk3DUVsJMjgxGpLSB54zsXK7d/RtTv5dMKgJ5Ixl1TATs5NhiRpK0ff6aGpuNRvSeZP63QPAlIHcm2aypgJ8c1PWvvf2vqo35Psn5aoXkSkHpmji/Q9KJ8dmqG4zk2GCmvrtMzb+62exgx4W+e1HEexN88iU9ZQPJKll1TATs5cpnG33AoWi4lXyMimicBAFKdI4ORSA2HwknGRkQ0TwIApDpHBiM9Lc1NptJemicBAFKdI4ORnpbmJlNpL82TAACpzpHBSE9awqe5pM9ClAPbVVZL8yQAQKpzZDDSk5bwPiPNfT54e+/y6jpd9PBruuapt3XHqipd89Tbuujh1xKyBTjNkwAAqc6RwYgUvuFQgTtLt325UJGe3f4KFX9ZbcckUn9ZbSICEponAQBSmcsYk/Q1n16vV263Wx6PRzk5OTE9d6vP6O2PDqli1yFJRiVnDpFc0nW/2Bzxvb/512L9v/95J2w1i0ttAcGme7+WkJmJVp+heRIAIGlYfX47tumZ37qa+qDOpT97fZcGZve19N6Kjw5aLqtNRFMjmicBAFKRo4ORcJ1LDx87YfEM1mYdKKsFACA8x+aMdNW51IpB/fpanoWgrBYAgPAcG4x0twur32dHT8hz9DhltQAA9JBjg5F6z7Eevd8l6cG1O7RoFmW1AAD0hGODkYYQjcui4U9OHdQ/Q7d/pVCuDvGGyyXd/pVCymoBAIjAscFI7mmZMTnPupp6/XxjrTo2XPUZ6ecbaxPSZwQAgFTm2GAkPyc2SaWrqz7pMgnW3xwNvY9dWwAAQG/j2NJe/54uXSWxprnUacbDzyVpUP++XS73JLrPCBKnvLouqD+N1JasvLisiKU5AIiSY2dG/Hu6uBS+W8htXy4M+XP/91dOGmHpWvQZ6V2SYQsAAOhNHBuMSKf2dHH369xxdWC/vpo8alCXe76UFuVbug59RnqPrvrT+F9jaQ4AouPYZZr2PEc7d1z1HD2hOSu2afn1U7Tp3q+F3POl1WdU4M5Svac55MPJvzcNfUZ6j0j9aViaA4DoOXpmxOqnXEkqGTNYl08aoZIxgwN9Q/xLPRJ9RpzC6pIbS3MAYJ2jg5FoPuWG41/qCbeU0xuTGZ1cRWJ1yY2lOQCwztHLNLH6lDtzfIGmF+WHXMrpbZxeReKvwmJpDgBix9EzI7H8lJue5gq5lNObUEXC0hwAxIOjgxH/p1w2uouMKpJTnLg0BwDx5OhlGv+n3DkrtsklBT1o+ZQbjCqSYE5amgOAeHN0MCKd+pTbMQ8i30F5EFZQRdKZf2kOANAzjg9GJD7lWkEVCQAgXghGPpcKn3Jbfca2gIkqEgBAvBCMpAi7S2rJrwEAxIujq2lSRbKU1FJFAgCIB2ZGklykklqX2kpqpxflJ2RWgvwaAECsEYwkuWQsqU2F/BoAQOpgmSbJUVILAOjtCEaSHCW1AIDejmAkydGyHgDQ2xGMJDk2ZgMA9HYEIymAkloAQG9GNU2K6FhSO6R/puSSDh5pUcWuQ5TXAgBSFsFICvGX1JZX1+n//c87tnVjBQAgllimSTHJ0o0VAIBYIRhJIZG6sUpt3VhbfaGOAAAgORGMpJBourECAJAqCEZSCN1YAQC9EcFICqEbKwCgN+pWMLJs2TKNHj1aWVlZKi4uVmVlZZfHP/bYYzrnnHOUnZ2tkSNH6q677lJzM5/eo0U3VgBAbxR1MPLCCy9o/vz5Wrx4sbZt26aJEydqxowZOnDgQMjjn3/+ed13331avHixduzYoV/+8pd64YUX9N3vfrfHg3caurECAHojlzEmqtKL4uJiffGLX9TPfvYzSZLP59PIkSP17W9/W/fdd1+n4+fNm6cdO3Zo/fr1gdfuvvtubd68WZs2bQp5jZaWFrW0tAS+93q9GjlypDwej3JycqIZbq9UXl2nJWtq6DMCAEhqXq9Xbrc74vM7qqZnx48f19atW7VgwYLAa2lpaSotLVVFRUXI93zpS1/SihUrVFlZqWnTpumjjz7Sq6++qhtuuCHsdZYuXaolS5ZEMzRH6diNddiALDqwAgBSVlTByMGDB9Xa2qq8vLyg1/Py8vS3v/0t5HuuvfZaHTx4UBdddJGMMTp58qRmz57d5TLNggULNH/+/MD3/pkRnOLvxgoAQKqLezXNhg0b9IMf/EBPPPGEtm3bppdeeklr167Vgw8+GPY9mZmZysnJCfoCAAC9U1QzI0OGDFF6err2798f9Pr+/fuVn58f8j2LFi3SDTfcoFtvvVWSNGHCBDU1Nen222/X9773PaWlUV0MAICTRRUJZGRkaOrUqUHJqD6fT+vXr1dJSUnI9xw9erRTwJGeni5JijJ3FgAA9EJR79o7f/583XTTTTr//PM1bdo0PfbYY2pqatLNN98sSbrxxhs1YsQILV26VJJUVlamRx99VJMnT1ZxcbF27typRYsWqaysLBCUAAAA54o6GLn66qv16aef6v7771d9fb0mTZqk8vLyQFLrnj17gmZCFi5cKJfLpYULF2rfvn0aOnSoysrK9J//+Z+x+y0AAEDKirrPiB2s1ikDAIDkYfX5TfYoAACwFcEIAACwFcEIAACwVdQJrHbwp7V4vV6bRwIAAKzyP7cjpaemRDDS2NgoSbSEBwAgBTU2Nsrtdof9eUpU0/h8Pn3yyScaMGCAXK7YbQbn3/Nm7969VOl0A/ev+7h3PcP96xnuX89w/6wzxqixsVHDhw/vsuN6SsyMpKWl6fTTT4/b+dn/pme4f93HvesZ7l/PcP96hvtnTVczIn4ksAIAAFsRjAAAAFs5OhjJzMzU4sWLlZmZafdQUhL3r/u4dz3D/esZ7l/PcP9iLyUSWAEAQO/l6JkRAABgP4IRAABgK4IRAABgK4IRAABgK4IRAABgK8cGI8uWLdPo0aOVlZWl4uJiVVZW2j2kmNu4caPKyso0fPhwuVwurV69Oujnxhjdf//9KigoUHZ2tkpLS/Xhhx8GHdPQ0KDrrrtOOTk5GjhwoP71X/9VR44cCTrm3Xff1Ze//GVlZWVp5MiR+uEPf9hpLL/97W81btw4ZWVlacKECXr11VejHksiLV26VF/84hc1YMAADRs2TFdccYU++OCDoGOam5s1d+5cDR48WKeddpr++Z//Wfv37w86Zs+ePZo1a5b69eunYcOG6Tvf+Y5OnjwZdMyGDRs0ZcoUZWZmauzYsXr22Wc7jSfSv1crY0mk5cuX67zzzgt0qCwpKdEf//jHqMbr1HvX0UMPPSSXy6U777wz8Br3L7wHHnhALpcr6GvcuHFRjdep985WxoFWrVplMjIyzNNPP23ef/99c9ttt5mBAwea/fv32z20mHr11VfN9773PfPSSy8ZSebll18O+vlDDz1k3G63Wb16tXnnnXfM17/+dVNYWGiOHTsWOGbmzJlm4sSJ5u233zZ/+ctfzNixY80111wT+LnH4zF5eXnmuuuuM9XV1WblypUmOzvb/Pd//3fgmDfffNOkp6ebH/7wh6ampsYsXLjQ9O3b17z33ntRjSWRZsyYYZ555hlTXV1tqqqqzGWXXWZGjRpljhw5Ejhm9uzZZuTIkWb9+vVmy5Yt5oILLjBf+tKXAj8/efKkGT9+vCktLTXbt283r776qhkyZIhZsGBB4JiPPvrI9OvXz8yfP9/U1NSYn/70pyY9Pd2Ul5cHjrHy7zXSWBLt97//vVm7dq35+9//bj744APz3e9+1/Tt29dUV1dbGq+T7117lZWVZvTo0ea8884zd9xxR+B17l94ixcvNueee66pq6sLfH366aeWx+vke2cnRwYj06ZNM3Pnzg1839raaoYPH26WLl1q46jiq2Mw4vP5TH5+vvnRj34UeO3w4cMmMzPTrFy50hhjTE1NjZFk/vrXvwaO+eMf/2hcLpfZt2+fMcaYJ554wgwaNMi0tLQEjrn33nvNOeecE/j+G9/4hpk1a1bQeIqLi823vvUty2Ox24EDB4wk88Ybbxhj2sbXt29f89vf/jZwzI4dO4wkU1FRYYxpCwbT0tJMfX194Jjly5ebnJycwP265557zLnnnht0rauvvtrMmDEj8H2kf69WxpIMBg0aZH7xi19w7yxqbGw0Z511llm3bp356le/GghGuH9dW7x4sZk4cWLIn3HvkpfjlmmOHz+urVu3qrS0NPBaWlqaSktLVVFRYePIEqu2tlb19fVB98Htdqu4uDhwHyoqKjRw4ECdf/75gWNKS0uVlpamzZs3B475yle+ooyMjMAxM2bM0AcffKDPPvsscEz76/iP8V/Hyljs5vF4JEm5ubmSpK1bt+rEiRNBYx43bpxGjRoVdP8mTJigvLy8wDEzZsyQ1+vV+++/Hzimq3tj5d+rlbHYqbW1VatWrVJTU5NKSkq4dxbNnTtXs2bN6vQ7cv8i+/DDDzV8+HCdeeaZuu6667Rnzx5J3Ltk5rhg5ODBg2ptbQ36hyZJeXl5qq+vt2lUief/Xbu6D/X19Ro2bFjQz/v06aPc3NygY0Kdo/01wh3T/ueRxmInn8+nO++8UxdeeKHGjx8vqW3MGRkZGjhwYNCxHX+v7t4br9erY8eOWfr3amUsdnjvvfd02mmnKTMzU7Nnz9bLL7+soqIi7p0Fq1at0rZt27R06dJOP+P+da24uFjPPvusysvLtXz5ctXW1urLX/6yGhsbuXdJrI/dAwCS3dy5c1VdXa1NmzbZPZSUcs4556iqqkoej0f/8z//o5tuuklvvPGG3cNKenv37tUdd9yhdevWKSsry+7hpJxLL7008N/nnXeeiouLdcYZZ+jFF19Udna2jSNDVxw3MzJkyBClp6d3yljev3+/8vPzbRpV4vl/167uQ35+vg4cOBD085MnT6qhoSHomFDnaH+NcMe0/3mksdhl3rx5+sMf/qDXX39dp59+euD1/Px8HT9+XIcPHw46vuPv1d17k5OTo+zsbEv/Xq2MxQ4ZGRkaO3aspk6dqqVLl2rixIl6/PHHuXcRbN26VQcOHNCUKVPUp08f9enTR2+88Yb+67/+S3369FFeXh73LwoDBw7U2WefrZ07d/JvL4k5LhjJyMjQ1KlTtX79+sBrPp9P69evV0lJiY0jS6zCwkLl5+cH3Qev16vNmzcH7kNJSYkOHz6srVu3Bo557bXX5PP5VFxcHDhm48aNOnHiROCYdevW6ZxzztGgQYMCx7S/jv8Y/3WsjCXRjDGaN2+eXn75Zb322msqLCwM+vnUqVPVt2/foDF/8MEH2rNnT9D9e++994ICunXr1iknJ0dFRUWBY7q6N1b+vVoZSzLw+XxqaWnh3kVwySWX6L333lNVVVXg6/zzz9d1110X+G/un3VHjhzRrl27VFBQwL+9ZGZ3Bq0dVq1aZTIzM82zzz5rampqzO23324GDhwYlD3dGzQ2Nprt27eb7du3G0nm0UcfNdu3bzcff/yxMaatnHbgwIHmlVdeMe+++665/PLLQ5b2Tp482WzevNls2rTJnHXWWUGlvYcPHzZ5eXnmhhtuMNXV1WbVqlWmX79+nUp7+/TpYx555BGzY8cOs3jx4pClvZHGkkhz5swxbrfbbNiwIahE8OjRo4FjZs+ebUaNGmVee+01s2XLFlNSUmJKSkoCP/eXCP7TP/2TqaqqMuXl5Wbo0KEhSwS/853vmB07dphly5aFLBGM9O810lgS7b777jNvvPGGqa2tNe+++6657777jMvlMv/7v/9rabxOvnehtK+mMYb715W7777bbNiwwdTW1po333zTlJaWmiFDhpgDBw5YGq+T752dHBmMGGPMT3/6UzNq1CiTkZFhpk2bZt5++227hxRzr7/+upHU6eumm24yxrSV1C5atMjk5eWZzMxMc8kll5gPPvgg6ByHDh0y11xzjTnttNNMTk6Oufnmm01jY2PQMe+884656KKLTGZmphkxYoR56KGHOo3lxRdfNGeffbbJyMgw5557rlm7dm3Qz62MJZFC3TdJ5plnngkcc+zYMfNv//ZvZtCgQaZfv37myiuvNHV1dUHn2b17t7n00ktNdna2GTJkiLn77rvNiRMngo55/fXXzaRJk0xGRoY588wzg67hF+nfq5WxJNItt9xizjjjDJORkWGGDh1qLrnkkkAgYnW8Tr13oXQMRrh/4V199dWmoKDAZGRkmBEjRpirr77a7Ny5M6rxOvXe2clljDH2zMkAAAA4MGcEAAAkF4IRAABgK4IRAABgK4IRAABgK4IRAABgK4IRAABgK4IRAABgK4IRAABgK4IRAABgK4IRAABgK4IRAABgq/8fEQWsv1dXzuQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we now have a total of 0 underpopped HDs and 0 overpopped HDs\n"
     ]
    }
   ],
   "source": [
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t] ]) for t in range(nHDs) ]\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP,ls=\"--\")\n",
    "plt.axvline(minDistrictPop,ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop,ls=\"dotted\")\n",
    "plt.show()\n",
    "\n",
    "print(\"updating our unitUse, underpopped and overpopped lists\")\n",
    "unitUse = [0.]*nUnits\n",
    "HDvPop = [0.]*nHDs\n",
    "stillUnder, stillOver = list(), list()\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += nDistricts * HDweight[t]\n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if HDvPop[t] < minDistrictPop:\n",
    "        stillUnder.append(t)\n",
    "    if HDvPop[t] > maxDistrictPop:\n",
    "        stillOver.append(t)\n",
    "print(\"unit use histogram\")\n",
    "plt.hist(unitUse, weights = unitPop)\n",
    "plt.show()\n",
    "print(\"unit use by pop\")\n",
    "plt.scatter(unitPop,unitUse)\n",
    "plt.show()\n",
    "print(\"we now have a total of\",len(stillUnder),\"underpopped HDs and\",len(stillOver),\"overpopped HDs\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "b0b99c26-000a-41ad-ba7a-daeb437aaeec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#see OH_schoolSnap99 for additional triage options; not needed here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "a90790d8-6f94-419b-8d88-d7476194c185",
   "metadata": {},
   "outputs": [],
   "source": [
    "splitUnitNo, splitUnitFrac, hasSplitUnit = [-999]*nHDs, [0.]*nHDs, [0]*nHDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "add6b13f-f45c-498e-82f3-c8778d0dc65e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "interm save in HDunitList format\n"
     ]
    }
   ],
   "source": [
    "print(\"interm save in HDunitList format\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"vtd\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"splitUnitNo\":splitUnitNo,\"splitUnitFrac\":splitUnitFrac,\n",
    "                       \"centroid x\":HDCPx, \"centroid y\":HDCPy, \"homeU\":homeU, \"homeC\":countyNo } )\n",
    "outname = STATE+str(int(nHDs))+\"_\"+str(nDistricts)+\"schoolReady2Patch.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "382c7962-d3dd-4fd0-b3b8-51f8b6e7eb2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "failedList = list() #not neded in school15; all HDs are groovy\n",
    "prePatchedList = [HDunitList[t].copy() for t in range(nHDs)] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "e5bba0ce-dc4a-494e-86b3-0fbefed0e032",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For now, we will freeze the HDs with split units or block-forced prior to patching.\n",
      "Start with underpatching. This algorithm simplified vs. vanillaHD to manage blockiness.\n",
      "Currently, we will stop patching when the overall sd of usage is less than 0.055\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated maxSD value 0.055\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this would currently cover a total of 80 units\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated number of units to try boosting usage 100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 152 units out of 610 with usage below 0.98\n",
      "maxExchangePop is currently 0.25 fraction of avgDistrictPop\n",
      "Let's further tighten the distro with exchanges up to 196657 = aDP* 0.25\n",
      "current avg and SD of unit usage are 0.99998 0.08141 . Now trying to increase up to 100 units' underusage\n",
      "all done trying2increase usage of unit 482 final usage = 0.98149 21 sec elapsed 112 68 complete, failed patches, unstarted= 27\n",
      "current avg and SD of usage are 0.99999 0.07663\n",
      "all done trying2increase usage of unit 347 final usage = 0.98116 56 sec elapsed 100 87 complete, failed patches, unstarted= 0\n",
      "current avg and SD of usage are 0.99998 0.07405\n",
      "all done trying2increase usage of unit 402 final usage = 0.98171 79 sec elapsed 93 27 complete, failed patches, unstarted= 89\n",
      "current avg and SD of usage are 0.99998 0.06962\n",
      "all done trying2increase usage of unit 403 final usage = 0.98097 111 sec elapsed 86 18 complete, failed patches, unstarted= 39\n",
      "current avg and SD of usage are 0.99999 0.06679\n",
      "all done trying2increase usage of unit 253 final usage = 0.98076 121 sec elapsed 79 3 complete, failed patches, unstarted= 2\n",
      "current avg and SD of usage are 0.99999 0.06261\n",
      "all done trying2increase usage of unit 103 final usage = 0.98121 134 sec elapsed 79 3 complete, failed patches, unstarted= 13\n",
      "current avg and SD of usage are 0.99999 0.06093\n",
      "all done trying2increase usage of unit 84 final usage = 0.9811 168 sec elapsed 90 62 complete, failed patches, unstarted= 10\n",
      "current avg and SD of usage are 0.99999 0.05889\n",
      "all done trying2increase usage of unit 604 final usage = 0.98083 188 sec elapsed 66 0 complete, failed patches, unstarted= 11\n",
      "current avg and SD of usage are 0.99999 0.05681\n",
      "all done trying2increase usage of unit 471 final usage = 0.93967 231 sec elapsed 42 406 complete, failed patches, unstarted= 220\n",
      "current avg and SD of usage are 0.99999 0.05096\n"
     ]
    }
   ],
   "source": [
    "print(\"For now, we will freeze the HDs with split units or block-forced prior to patching.\")\n",
    "print(\"Start with underpatching. This algorithm simplified vs. vanillaHD to manage blockiness.\")\n",
    "\n",
    "maxSD = 0.055  #0.07  #0.05   #adjust down if distro already tight, adjust up if blocky\n",
    "print(\"Currently, we will stop patching when the overall sd of usage is less than\",maxSD)\n",
    "maxSD = float(input(\"enter updated maxSD value\"))\n",
    "stopMinUse = 1. - maxSD\n",
    "#print(\"we will stop patching on individual underused units when their usage exceeds\",r5(stopMinUse))\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        maxNtries +=1\n",
    "print(\"this would currently cover a total of\",maxNtries,\"units\")\n",
    "maxNtries = int(input(\"enter updated number of units to try boosting usage\"))\n",
    "stopMinUse = 0.98 #float(input(\"enter updated stopMinUse value for ending usage boost on a unit; I reco 0.98\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] < stopMinUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage below\",stopMinUse)\n",
    "maxExchangePop = 0.25*aDP #this is widened vs. vanillaHD to overcome blockiness\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "debug1 = 10 #int(input(\"enter 1 to print out stats for every patched HD, otherwise enter reporting frequency\"))\n",
    "print(\"Let's further tighten the distro with exchanges up to\",int(maxExchangePop),\"= aDP*\",r3(maxExchangePop/aDP) ) \n",
    "maxGap = aDP - minDistrictPop #0.9 * np.median(unitPop) \n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "nonfrozenList = list()  \n",
    "for t in popHDlist:\n",
    "    if hasSplitUnit[t] != 1 and t not in failedList:  #don't mess with the small number of HDs that had major challenges\n",
    "        nonfrozenList.append(t)\n",
    "\n",
    "attemptedSmallUs, startTime = list(), time.time()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to increase up to\",maxNtries,\"units' underusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedSmallUs) < maxNtries:\n",
    "    #simplified vs. vanillaHD - always work on the lowest-used unit.\n",
    "    eligUnitUse = unitUse.copy()\n",
    "    for u in range(nUnits):\n",
    "        if u in attemptedSmallUs: #we already tried this one, so lock it out\n",
    "            eligUnitUse[u] = 999.  #preventing re-selection\n",
    "    UUU = eligUnitUse.index(np.min(eligUnitUse))\n",
    "    attemptedSmallUs.append(UUU)\n",
    "    UUUc = [UUU] + unitNbrs[UUU]\n",
    "    if len(attemptedSmallUs) % debug1 == 0:\n",
    "        print(\"begin usage increase for unit\",UUU,\"with usage\",r5(unitUse[UUU]),\". Sec, total UU units tried =\",\n",
    "              int(time.time()-startTime),len(attemptedSmallUs) )\n",
    "    for u in UUUc.copy():\n",
    "        if unitUse[u] > 1.:\n",
    "            UUUc.remove(u)  #...drop any overused neighbors of the primary UUU from the target sheddable cluster\n",
    "    uuuHDs, uuuDists, UUUcSet = list(), list(), set(UUUc)\n",
    "    for t in nonfrozenList: #popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if UUU not in HDunitList[t]:\n",
    "            HDadjoinSet = set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(UUUcSet) ) > 0: #the UUU or one of its underused 1-neighbors adjoins this HD\n",
    "                uuuHDs.append(t)  #Note: we'll check later if we can contiguously pick up the UUU's cluster\n",
    "                uuuDists.append( unitCP[UUU].distance(hdCP[t]) / avgDist[t] )\n",
    "        idx0 = np.argsort(uuuDists)\n",
    "    hasCandidates = True\n",
    "    if len(uuuDists) == 0:  #this underused unit is buried inside others; skip it\n",
    "        hasCandidates = False\n",
    "        print(\"   Couldn't find any HDs adjacent to underused unit\",UUU)\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo = 0,0,0,-1\n",
    "    while unitUse[UUU] < stopMinUse and idxNo < 0.8*len(uuuHDs) and hasCandidates: #arbitrarily only examine 80% closest of HDs\n",
    "        excess, giveUpOnShedding = 99*aDP, True  #default = failed swap\n",
    "        idxNo += 1\n",
    "        if idxNo % 40 == 0 and debug1 == 1:\n",
    "            print(\"try to add unit\",UUU,\"from\",abs(idxNo),\"th HD.  Usage up to\",unitUse[UUU] )\n",
    "        t = uuuHDs[idx0[idxNo]]\n",
    "        trySet = set(HDunitList[t]).union(UUUcSet)\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        loopUUUc = UUUc.copy()  #default; we will add whole cluster\n",
    "        if not (contig and complementContig): #we can't add whole cluster; neighbors may be enclavy.   Try just adding the UUU\n",
    "            trySet = set(HDunitList[t]).union({UUU})\n",
    "            contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "            loopUUUc = [UUU]\n",
    "        if contig and complementContig:  #we can at least add the cluster, let's go for more\n",
    "            HDuuuSet = set(loopUUUc).difference(set(HDunitList[t]))  #the subset of the UUU cluster that adjoins (NOT in) this HD\n",
    "            HDuuuCpop = np.sum([unitPop[u] for u in HDuuuSet])\n",
    "            giveUpOnAdding = False            \n",
    "            addCandidates, addUseDists = list(), list()\n",
    "            uuCandidates = getAdjoiners(trySet, unitNbrs)  #any adjoiner can be picked up, even if far from UUUc\n",
    "            for u in uuCandidates:\n",
    "                if HDuuuCpop + unitPop[u] <= maxExchangePop:\n",
    "                    addCandidates.append(u)  #Below's relative scoring of use and distance is a bit arbitrary\n",
    "                    addUseDists.append((unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t])  #bias toward close, underused\n",
    "            if len(addCandidates) == 0:\n",
    "                giveUpOnAdding = True\n",
    "            while HDuuuCpop < maxExchangePop and not giveUpOnAdding:\n",
    "                addNneighbors = [len( set(unitNbrs[addC]).intersection(trySet) ) for addC in addCandidates ]\n",
    "                addScores = addUseDists.copy()\n",
    "                for jjj, u in enumerate(addCandidates):\n",
    "                    if addNneighbors[jjj] == 1:\n",
    "                        addScores[jjj] += 0.4321    #discourage growing fingers\n",
    "                #print(\"HDuuuCpop is now\",HDuuuCpop)\n",
    "                idx, ij, notYetPicked = np.argsort(addScores), 0, True\n",
    "                while ij < 0.5*len(addScores) and notYetPicked:\n",
    "                    listNo = idx[ij]   #work from low to high score\n",
    "                    addU = addCandidates[listNo]\n",
    "                    cContig = wontEnclave(addU, list(trySet), unitNbrs, borderUnits)  #4/20/24 sub this in as faster? vs below line\n",
    "                    #contig,cContig, __, ___ = enclaveCheck(list(trySet.union({addU}) ), unitNbrs)  #4/20/24 this is unnec slow\n",
    "                    if cContig: #contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDuuuCpop += unitPop[addU]\n",
    "                        HDuuuSet.add(addU)\n",
    "                        trySet.add(addU)\n",
    "                        del addUseDists[addCandidates.index(addU)]\n",
    "                        del addCandidates[addCandidates.index(addU)]                        \n",
    "                        for uu in list(set(unitNbrs[addU]).difference(trySet) ): \n",
    "                            if uu not in addCandidates and unitPop[uu] + HDuuuCpop < maxExchangePop and unitUse[uu] < 1.01:\n",
    "                                addCandidates.append(uu)\n",
    "                                addUseDists.append( (unitUse[uu]-1.) + 0.1*unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnAdding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            addSet = HDuuuSet.copy()  #we're done building the list of underused units to add to this HD\n",
    "            trySet = set(HDunitList[t]).union(addSet) \n",
    "            excess = np.sum([unitPop[u] for u in trySet]) - aDP\n",
    "            shedCandidates, shedScores, giveUpOnShedding = list(), list(), False\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if unitPop[u] <= excess + maxGap and u != homeU[t]:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward OVERUSED, far\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            shedSet = set()\n",
    "            while excess > maxGap and not giveUpOnShedding:\n",
    "                #print(\"excess pop is now\",excess,\"for HD\",t)\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    if excess - unitPop[shedU] >= -1*maxGap:\n",
    "                        contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                        if contig and cContig:\n",
    "                            notYetPicked = False\n",
    "                            excess -= unitPop[shedU]\n",
    "                            trySet.remove(shedU)\n",
    "                            shedSet.add(shedU)\n",
    "                            del shedScores[shedCandidates.index(shedU)]\n",
    "                            del shedCandidates[shedCandidates.index(shedU)]\n",
    "                            newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                            for u in newSheddables:\n",
    "                                if excess - unitPop[u] >= -1*maxGap and u not in shedCandidates + [homeU[t]]:\n",
    "                                    shedCandidates.append(u)  #we'll check enclavity if ever picked\n",
    "                                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                            for kkk, u in enumerate(shedCandidates): #checking if this shed eliminates high-pop future sheds ...\n",
    "                                if excess - unitPop[u] < -1*maxGap:\n",
    "                                    del shedScores[kkk]\n",
    "                                    del shedCandidates[kkk]\n",
    "                    ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all shedcandidates would create a discontig, so can't shed enough units to square pop\n",
    "            legitSwap = False\n",
    "            currPop = np.sum([ unitPop[u] for u in trySet] )\n",
    "            if abs(currPop - aDP) <= maxGap: \n",
    "                contig,cContig, __, ___ = enclaveCheck(list(trySet), unitNbrs) \n",
    "                if contig and cContig:\n",
    "                    legitSwap = True\n",
    "            if legitSwap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t] = np.sum([ unitPop[u] for u in HDunitList[t] ])\n",
    "                nPatchSuccess +=1\n",
    "                #print(\"we added\",addSet,\"and shed units\",shedSet,\"from HD\",t)\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "        else:  #adding neither the UUU cluster or just the UUU worked; couldn't even start\n",
    "            nCouldntStart +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "\n",
    "    print(\"all done trying2increase usage of unit\",UUU,\"final usage =\",r5(unitUse[UUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"complete, failed patches, unstarted=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "6f7f2f60-aa6c-4d46-b2e3-7d2f68841e68",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " patched use avg 0.99999 and SD 0.05096\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 8941\n",
      "working on HD 800 out of 8941\n",
      "working on HD 1600 out of 8941\n",
      "working on HD 2400 out of 8941\n",
      "working on HD 3200 out of 8941\n",
      "working on HD 4000 out of 8941\n",
      "working on HD 4800 out of 8941\n",
      "working on HD 5600 out of 8941\n",
      "working on HD 6400 out of 8941\n",
      "working on HD 7200 out of 8941\n",
      "working on HD 8000 out of 8941\n",
      "working on HD 8800 out of 8941\n",
      "all done checking HD and complement contiguity for all 8941 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse = [0.]*nUnits, [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts\n",
    "    if hasSplitUnit[t] == 1:\n",
    "        unitUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "#    for u in unpatchedHDlist[t]:\n",
    "#        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "#plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "#unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\" patched use avg\", r5(patchedAvg),\"and SD\",r5(patchedSD) )\n",
    "#print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.axvline(minDistrictPop, ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop, ls=\"dotted\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "5faa4270-4a8f-4777-b514-6d72cd77718b",
   "metadata": {},
   "outputs": [],
   "source": [
    "underpatchedUnitList = [HDunitList[t].copy() for t in range(nHDs) ] #safekeeping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "63ff3e3c-81f2-4f54-9a92-d07fdeb12cc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current avg and SD of usage are 0.99999 0.05096\n"
     ]
    }
   ],
   "source": [
    "HDunitList = [underpatchedUnitList[t].copy() for t in range(nHDs) ] #restart\n",
    "HDvPop = [np.sum([unitPop[u] for u in HDunitList[t]]) for t in range(nHDs)]\n",
    "unitUse = [0.]*nUnits\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        unitUse[u] += HDweight[t] * nDistricts\n",
    "    if hasSplitUnit[t] == 1:\n",
    "        unitUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "\n",
    "\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "6e54e08e-d07a-418f-b730-7bbc9d336a23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we are still biased toward high overuse for large units, so do another round of overpatch\n",
      "0.08 = default maxSD. Reco'ing 0.05 for blocky, 0.08 when only partially underpatched before this\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter maxSD 0.04\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "default use threshold for moving on to next overused unit is 1.02\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "enter updated stopMaxUse value 1.02\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are currently 141 units out of 610 with usage above 1.02\n",
      "maxExchangePop is currently 0.25 fraction of avgDistrictPop\n",
      "this block reduces overuse, with exchanges up to 196657\n",
      "current avg and SD of unit usage are 0.99999 0.05096 . Now trying to reduce up to 141 units' overusage\n",
      "starting to reduce usage for unit 158 with usage 1.22074 . Total units tried = 1\n",
      "try to drop unit 158 from 0 th HD out of 729 possible.  usage down to 1.2207431228982548\n",
      "try to drop unit 158 from 90 th HD out of 729 possible.  usage down to 1.2065979696673632\n",
      "try to drop unit 158 from 180 th HD out of 729 possible.  usage down to 1.2021867463626816\n",
      "try to drop unit 158 from 270 th HD out of 729 possible.  usage down to 1.1931685278833104\n",
      "try to drop unit 158 from 360 th HD out of 729 possible.  usage down to 1.185609699707623\n",
      "try to drop unit 158 from 450 th HD out of 729 possible.  usage down to 1.1780559565155708\n",
      "try to drop unit 158 from 540 th HD out of 729 possible.  usage down to 1.1726137527785854\n",
      "all done trying to reduce usage of unit 158 final usage = 1.17261 10 sec elapsed 27 421 successful, failed patches, couldn't start= 136\n",
      "current avg and SD of usage are 0.99998 0.04989\n",
      "starting to reduce usage for unit 131 with usage 1.18887 . Total units tried = 2\n",
      "try to drop unit 131 from 0 th HD out of 577 possible.  usage down to 1.1888653604809174\n",
      "try to drop unit 131 from 90 th HD out of 577 possible.  usage down to 1.1825701507389048\n",
      "try to drop unit 131 from 180 th HD out of 577 possible.  usage down to 1.1825701507389048\n",
      "try to drop unit 131 from 270 th HD out of 577 possible.  usage down to 1.1708848583421778\n",
      "try to drop unit 131 from 360 th HD out of 577 possible.  usage down to 1.086405482696837\n",
      "all done trying to reduce usage of unit 131 final usage = 1.01959 23 sec elapsed 86 325 successful, failed patches, couldn't start= 18\n",
      "current avg and SD of usage are 0.99999 0.0455\n",
      "starting to reduce usage for unit 196 with usage 1.16522 . Total units tried = 3\n",
      "try to drop unit 196 from 0 th HD out of 650 possible.  usage down to 1.1652163728333869\n",
      "try to drop unit 196 from 90 th HD out of 650 possible.  usage down to 1.1231203357984454\n",
      "try to drop unit 196 from 180 th HD out of 650 possible.  usage down to 1.0871567890291471\n",
      "try to drop unit 196 from 270 th HD out of 650 possible.  usage down to 1.0524403344967082\n",
      "try to drop unit 196 from 360 th HD out of 650 possible.  usage down to 1.0441657948741778\n",
      "try to drop unit 196 from 450 th HD out of 650 possible.  usage down to 1.0233631268171695\n",
      "all done trying to reduce usage of unit 196 final usage = 1.01759 33 sec elapsed 87 387 successful, failed patches, couldn't start= 5\n",
      "current avg and SD of usage are 0.99999 0.04177\n",
      "starting to reduce usage for unit 525 with usage 1.16272 . Total units tried = 4\n",
      "try to drop unit 525 from 0 th HD out of 706 possible.  usage down to 1.162720917113773\n",
      "try to drop unit 525 from 90 th HD out of 706 possible.  usage down to 1.1612564418264544\n",
      "try to drop unit 525 from 180 th HD out of 706 possible.  usage down to 1.1612564418264544\n",
      "try to drop unit 525 from 270 th HD out of 706 possible.  usage down to 1.1403927539658083\n",
      "try to drop unit 525 from 360 th HD out of 706 possible.  usage down to 1.1155284552291287\n",
      "try to drop unit 525 from 450 th HD out of 706 possible.  usage down to 1.0929918077520675\n",
      "try to drop unit 525 from 540 th HD out of 706 possible.  usage down to 1.088616179332842\n",
      "all done trying to reduce usage of unit 525 final usage = 1.08139 44 sec elapsed 44 462 successful, failed patches, couldn't start= 59\n",
      "current avg and SD of usage are 0.99999 0.03952\n"
     ]
    }
   ],
   "source": [
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  \n",
    "print(\"we are still biased toward high overuse for large units, so do another round of overpatch\")\n",
    "#SECOND PATCHING BLOCK -- OVERUSERS.  applies a suppression of LONG STRINGS.  \n",
    "maxSD = 0.08 #0.06  #0.08  #0.04 \n",
    "print(maxSD,\"= default maxSD. Reco'ing 0.05 for blocky, 0.08 when only partially underpatched before this\")\n",
    "maxSD = float(input(\"enter maxSD\"))\n",
    "stopMaxUse = 1.02 #1.+  2.* maxSD  #0.5*maxSD  #don't be too aggressive; may create long chains\n",
    "print(\"default use threshold for moving on to next overused unit is\",r5(stopMaxUse) )\n",
    "stopMaxUse = float(input(\"enter updated stopMaxUse value\"))\n",
    "nInPlay = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        nInPlay +=1\n",
    "print(\"there are currently\",nInPlay,\"units out of\",nUnits,\"with usage above\",stopMaxUse)\n",
    "nBigUsers = 5\n",
    "maxExchangePop = 0.25*aDP  #0.15 or 0.25 is debatable.  use 0.15 unless blocky\n",
    "print(\"maxExchangePop is currently\",r5(maxExchangePop/aDP),\"fraction of avgDistrictPop\")\n",
    "print(\"this block reduces overuse, with exchanges up to\",int(maxExchangePop)) #\n",
    "maxGap = aDP - minDistrictPop  #0.9 * np.median(unitPop) # = aDP - minDistrictPop\n",
    "maxNtries = 0\n",
    "for u in range(nUnits):\n",
    "    if unitUse[u] > stopMaxUse:\n",
    "        maxNtries +=1\n",
    "currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "nonfrozenList = list()\n",
    "for t in popHDlist:\n",
    "    if t not in hasSplitUnit: #hasSplitUnit[t] != 1:  #don't mess with the small number of HDs that have to have a split muni\n",
    "        nonfrozenList.append(t)\n",
    "        \n",
    "attemptedBigUs = list()\n",
    "print(\"current avg and SD of unit usage are\",r5(currAvg), r5(currSD),\". Now trying to reduce up to\",maxNtries,\"units' overusage\" )\n",
    "startTime = time.time()\n",
    "while currSD > maxSD and len(attemptedBigUs) < maxNtries: \n",
    "    #simplified vs. vanillaHD - just take the biggest overuser\n",
    "    eligUnitUse = unitUse.copy()\n",
    "    for u in range(nUnits):\n",
    "        if u in attemptedBigUs: #we already tried this one, so lock it out\n",
    "            eligUnitUse[u] = -999.  #preventing re-selection\n",
    "    OUU = eligUnitUse.index(np.max(eligUnitUse))\n",
    "    attemptedBigUs.append(OUU)  #so we don't try this unit again in a future loop\n",
    "    if unitPop[OUU] > 0.25 * aDP:  #some overusers are high-pop.  If so, will drop only this unit, not any neighbors\n",
    "        OUUc = [OUU]\n",
    "    else:\n",
    "        OUUc = [OUU] + unitNbrs[OUU]\n",
    "    print(\"starting to reduce usage for unit\",OUU,\"with usage\",r5(unitUse[OUU]),\". Total units tried =\",len(attemptedBigUs) )\n",
    "    for u in OUUc.copy():\n",
    "        if unitUse[u] < 1.:\n",
    "            OUUc.remove(u)  #...drop any underused neighbors of the primary OUU from the target sheddable cluster\n",
    "    OUUcSet = set( OUUc)  #for muni-snap, overused are isolated.  So don't bother adding two-level neighbors to cluster\n",
    "    ouuHDs, ouuDists = list(), list()\n",
    "    for t in nonfrozenList: #popHDlist:  #finding all HDs with at least one cluster member on the boundary\n",
    "        if OUU in HDunitList[t] and homeU[t] != OUU: #can't drop the home muni\n",
    "            HDadjoinSet =   set( getAdjoiners(HDunitList[t],unitNbrs) )\n",
    "            OUUcAdjoinSet = set( getAdjoiners(list(OUUcSet),unitNbrs) )\n",
    "            if len(HDadjoinSet.intersection(OUUcAdjoinSet) ) > 0: #the OUU or one of its overused 1-neighbors adjoins the complement\n",
    "                ouuHDs.append(t)  #so we can shed the OOUc to the complement\n",
    "                ouuDists.append( unitCP[OUU].distance(hdCP[t]) / avgDist[t] )\n",
    "    nPatchSuccess, nPatchFail, nCouldntStart, idxNo, idx0 = 0, 0, 0, 0, np.argsort(ouuDists)\n",
    "    while unitUse[OUU] > stopMaxUse and idxNo > -0.8*len(ouuHDs) : #arbitrarily only examine 80% of HDs, biasing farthest ones       \n",
    "        if idxNo % 90 == 0:\n",
    "            print(\"try to drop unit\",OUU,\"from\",abs(idxNo),\"th HD out of\",len(ouuHDs),\"possible.  usage down to\",unitUse[OUU] )\n",
    "        idxNo -=1\n",
    "        t = ouuHDs[idx0[idxNo]]\n",
    "        thisLoopOUUc = [OUU]\n",
    "        possibleOUUc = list((  OUUcSet.intersection(set(HDunitList[t]) )  ).difference( {homeU[t]} )) #again, homeU is untouchable\n",
    "        if np.sum([unitPop[u] for u in possibleOUUc]) < 0.25*aDP:\n",
    "            thisLoopOUUc = possibleOUUc.copy() #we can add the whole non-HDincluded cluster (-homeU) without exceeding 0.25 aDP            \n",
    "        trySet = set(HDunitList[t]).difference(set(thisLoopOUUc))\n",
    "        contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        if not (contig and complementContig):  #dropping the cluster creates a problem (likely an HD discontig).  Try something simpler ...\n",
    "            if len(set(unitNbrs[OUU]).intersection(HDadjoinSet) )  > 0:  #Yay! The OUU itself on border.  Try dropping just it, not the full cluster\n",
    "                HDouuSet = {OUU}\n",
    "                trySet = set(HDunitList[t]).difference( {OUU} )\n",
    "                contig,complementContig, __, ___ = enclaveCheck(list(trySet), unitNbrs)\n",
    "        else:\n",
    "            HDouuSet = set(HDunitList[t]).intersection(set(thisLoopOUUc))  #set(OUUc)\n",
    "        if contig and complementContig:  #we can at least drop the cluster, let's go for more\n",
    "            #HDouuSet = set(HDunitList[t]).intersection(set(OUUc))  #the subset of the OUU cluster that's in this HD.  Defined above\n",
    "            HDouuCpop = np.sum([unitPop[u] for u in HDouuSet])\n",
    "            giveUpOnShedding = False            \n",
    "            shedCandidates, shedScores = list(), list()\n",
    "            bdryCandidates = getBdryNonEdgers(trySet, unitNbrs)  #new - any boundary unit can be shed, even if far from OUUc\n",
    "            for u in bdryCandidates:\n",
    "                if HDouuCpop + unitPop[u] <= maxExchangePop and u != homeU[t]:\n",
    "                    shedCandidates.append(u)\n",
    "                    shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])  #bias toward far, overused\n",
    "            if len(shedCandidates) == 0:\n",
    "                giveUpOnShedding = True\n",
    "            while HDouuCpop < maxExchangePop and not giveUpOnShedding:\n",
    "                idx, ij, notYetPicked = np.argsort(shedScores), 0, True\n",
    "                while ij < len(shedScores) and notYetPicked:\n",
    "                    listNo = idx[-1-ij]   #work from high to low score\n",
    "                    shedU = shedCandidates[listNo]\n",
    "                    if shedScores[listNo] <= 0:  #we're delving into the underused; stop adding to shed list\n",
    "                        break\n",
    "                    contig,cContig, __, ___ = enclaveCheck(list(trySet.difference({shedU}) ), unitNbrs)\n",
    "                    if contig and cContig:\n",
    "                        notYetPicked = False\n",
    "                        HDouuCpop += unitPop[shedU]\n",
    "                        #print(\"shedding unit\",shedU,\"from HD\",t,\"total shed Pop now\", HDouuCpop)\n",
    "                        HDouuSet.add(shedU)\n",
    "                        trySet.remove(shedU)\n",
    "                        del shedScores[shedCandidates.index(shedU)]\n",
    "                        del shedCandidates[shedCandidates.index(shedU)]\n",
    "                        newSheddables = set(unitNbrs[shedU]).intersection(trySet)\n",
    "                        for u in newSheddables:\n",
    "                            if HDouuCpop + unitPop[u] <= maxExchangePop and u not in shedCandidates and u != homeU[t]:\n",
    "                                shedCandidates.append(u)\n",
    "                                shedScores.append((unitUse[u] - 1.) * unitCP[u].distance(hdCP[t])/avgDist[t])\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    giveUpOnShedding = True  #all candidates would create a discontig, so can't shed any more units\n",
    "            shedSet = HDouuSet.copy()  #set(OUUc).intersection(set(HDunitList[t]))\n",
    "            trySet = set(HDunitList[t]).difference(shedSet) \n",
    "            gap = aDP - np.sum([unitPop[u] for u in trySet])\n",
    "            nearHDlist, nearHDscore = list(), list()  #these will be dynamic lists of the nearby underused units\n",
    "            for u in trySet:\n",
    "                for uu in unitNbrs[u]:\n",
    "                    if uu not in HDunitList[t] and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:\n",
    "                        nearHDlist.append(uu)\n",
    "                        nearHDscore.append(5.*(unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] )\n",
    "                        #bias toward UNDERUSED, close\n",
    "            addedSet = set( )    \n",
    "            stillGoing = True \n",
    "            while gap > maxGap and len(nearHDlist) > 0 and stillGoing:   #add the lowest-scoring neighboring underused unit until we've roughly squared the HDpop\n",
    "                nearHDscore2 = nearHDscore.copy()\n",
    "                for ij, uu in enumerate(nearHDlist):\n",
    "                    nHDnbrs = len( set(unitNbrs[uu]).intersection(trySet) )\n",
    "                    if nHDnbrs == 1 :  #BIAS AGAINST CREATING A SLIM CHAIN\n",
    "                        nearHDscore2[ij] *= 0.5   #make this score closer to zero (less negative) \n",
    "                idx, ij, notYetPicked = np.argsort(nearHDscore2), 0, True   #originally, used nearHDscore itself here\n",
    "                while ij < len(nearHDscore) and notYetPicked:        \n",
    "                    listNo = idx[ij]   #nearHDscore.index(np.min(nearHDscore))\n",
    "                    unitNoToAdd = nearHDlist[listNo]  #add this unit ...                            \n",
    "                    canAdd  = wontEnclave(unitNoToAdd, list(trySet), unitNbrs, borderUnits)\n",
    "                    if canAdd:\n",
    "                        notYetPicked = False\n",
    "                    else:\n",
    "                        ij +=1\n",
    "                if notYetPicked:\n",
    "                    stillGoing = False  #can't add any more units; we can't without creating an enclave\n",
    "                else:\n",
    "                    gap -= unitPop[unitNoToAdd]\n",
    "                    addedSet.add(unitNoToAdd)\n",
    "                    trySet.add( unitNoToAdd)\n",
    "                    for uu in unitNbrs[unitNoToAdd]:             # ... and add its nonHD neighbors to future candidates\n",
    "                        if uu not in trySet and uu not in nearHDlist and unitPop[uu] < gap + maxGap and unitUse[uu] < 1.01:  \n",
    "                            nearHDlist.append(uu)\n",
    "                            nearHDscore.append(5.* (unitUse[uu]-1.) + unitCP[uu].distance(hdCP[t]) / avgDist[t] ) \n",
    "                            #bias toward UNDERUSED, ~close\n",
    "                    del nearHDscore[nearHDlist.index(unitNoToAdd)]        \n",
    "                    del nearHDlist[ nearHDlist.index(unitNoToAdd) ]\n",
    "                    for ijj, uu in enumerate(nearHDlist.copy()):\n",
    "                        if unitPop[uu] > gap + maxGap:   #with the added pop from another unit, this unit is now too big to add\n",
    "                            del nearHDscore[nearHDlist.index(uu)]\n",
    "                            del nearHDlist[ nearHDlist.index(uu)]\n",
    "            currPop = np.sum([unitPop[u] for u in trySet])\n",
    "            if abs(currPop - aDP) <= maxGap:\n",
    "                for u in shedSet:\n",
    "                    unitUse[u] -= HDweight[t] * nDistricts\n",
    "                for u in addedSet:\n",
    "                    unitUse[u] += HDweight[t] * nDistricts\n",
    "                HDunitList[t] = list(trySet)\n",
    "                HDvPop[t]    = currPop\n",
    "                nPatchSuccess +=1\n",
    "            else:\n",
    "                nPatchFail +=1\n",
    "            # end of shed + add patching on this HD\n",
    "            #print(\"shed and patched for HD, OUU\",t,OUU)\n",
    "        else:\n",
    "            nCouldntStart +=1\n",
    "            #print(\"Due to discontiguity, can't drop OUU cluster for HD, OUU\", t, OUU)\n",
    "    print(\"all done trying to reduce usage of unit\",OUU,\"final usage =\",r5(unitUse[OUU]),int(time.time()-startTime),\"sec elapsed\",\n",
    "         nPatchSuccess,nPatchFail,\"successful, failed patches, couldn't start=\",nCouldntStart)\n",
    "    currAvg, currSD = getWeightedAvgAndSD(unitUse,unitPop)\n",
    "    print(\"current avg and SD of usage are\",r5(currAvg), r5(currSD) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "35140f52-2cb9-40e6-be15-ac02ba88316c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " patched use avg 0.99999 and SD 0.03952\n",
      "And here is the pop distro\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking contiguity of final HDs\n",
      "working on HD 0 out of 8941\n",
      "working on HD 800 out of 8941\n",
      "working on HD 1600 out of 8941\n",
      "working on HD 2400 out of 8941\n",
      "working on HD 3200 out of 8941\n",
      "working on HD 4000 out of 8941\n",
      "working on HD 4800 out of 8941\n",
      "working on HD 5600 out of 8941\n",
      "working on HD 6400 out of 8941\n",
      "working on HD 7200 out of 8941\n",
      "working on HD 8000 out of 8941\n",
      "working on HD 8800 out of 8941\n",
      "all done checking HD and complement contiguity for all 8941 HDs\n"
     ]
    }
   ],
   "source": [
    "patchedUse, unpUse, HDvPop = [0.]*nUnits, [0.]*nUnits, [0.]*nHDs\n",
    "for t in popHDlist:\n",
    "    if homeU[t] not in HDunitList[t]:\n",
    "        print(\"WARNING - we lost home unit\",homeU[t],\"from HD\",t)\n",
    "    for u in HDunitList[t]:\n",
    "        patchedUse[u] += HDweight[t] * nDistricts       \n",
    "        HDvPop[t] += unitPop[u]\n",
    "    if hasSplitUnit[t] == 1:\n",
    "        unitUse[splitUnitNo[t]] -= (1. - splitUnitFrac[t]) * nDistricts * HDweight[t]\n",
    "        HDvPop[t]               -= (1. - splitUnitFrac[t]) * unitPop[splitUnitNo[t]]\n",
    "#    for u in unpatchedHDlist[t]:\n",
    "#        unpUse[u] += HDweight[t] * nDistricts\n",
    "plt.hist(patchedUse,bins=50, label=\"patched\",histtype=\"step\")\n",
    "#plt.hist(unpUse, bins=50, label=\"unpatched\",histtype=\"step\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "patchedAvg, patchedSD = getWeightedAvgAndSD(patchedUse,unitPop)\n",
    "#unpatchedAvg, unpatchedSD = getWeightedAvgAndSD(unpUse,unitPop)\n",
    "print(\" patched use avg\", r5(patchedAvg),\"and SD\",r5(patchedSD) )\n",
    "#print(\"unpatched, patched use avgs are\",r5(unpatchedAvg), r5(patchedAvg),\"and their SDs are\",r5(unpatchedSD), r5(patchedSD) )\n",
    "print(\"And here is the pop distro\")\n",
    "\n",
    "plt.hist([HDvPop[t] for t in popHDlist])\n",
    "plt.axvline(aDP, ls=\"--\",color=\"orange\")\n",
    "plt.axvline(minDistrictPop, ls=\"dotted\")\n",
    "plt.axvline(maxDistrictPop, ls=\"dotted\")\n",
    "plt.show()\n",
    "print(\"checking contiguity of final HDs\")\n",
    "for t in popHDlist:\n",
    "    if t%800 == 0:\n",
    "        print(\"working on HD\",t,\"out of\",nHDs)\n",
    "    unbroken, noEnclave, sList, eList = enclaveCheck(HDunitList[t], unitNbrs)  #these HDunitLists now appear to be sets\n",
    "    if not unbroken or not noEnclave:\n",
    "        print(\"uh-oh, HD\",t,\"has contiguity, complement-contiguity of\",unbroken, noEnclave)\n",
    "print(\"all done checking HD and complement contiguity for all\",nHDs,\"HDs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "92e9e7c1-c3a3-4039-a1d7-1dfe5d9a727b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "patched save in HDunitList format\n"
     ]
    }
   ],
   "source": [
    "print(\"patched save in HDunitList format\")\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"vtd\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDunitList\":HDunitList,\n",
    "                      \"splitUnitNo\":splitUnitNo,\"splitUnitFrac\":splitUnitFrac,\n",
    "                       \"centroid x\":HDCPx, \"centroid y\":HDCPy, \"homeU\":homeU, \"homeC\":countyNo } )\n",
    "outname = STATE+str(int(nHDs))+\"_\"+str(nDistricts)+\"schoolPatch04.csv\" \n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "2f2b0f2e-20ed-4220-846e-4a0bf687d1d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now write the final vtd lists to a file.  In this version, we had no splits, so assign vtds here\n"
     ]
    }
   ],
   "source": [
    "print(\"now write the final vtd lists to a file.  In this version, we had no splits, so assign vtds here\")\n",
    "finalVTDlist = [list() for t in range(nHDs)]\n",
    "for t in range(nHDs):\n",
    "    for u in HDunitList[t]:\n",
    "        finalVTDlist[t] += unitTractList[u]\n",
    "tList = [t for t in range(nHDs)]\n",
    "outDF = pd.DataFrame( {\"vtd\":tList,\"HDweight\":HDweight,\"HDvPop\":HDvPop,\"HDvtdList\":finalVTDlist,\n",
    "                      \"centroid x\":HDCPx, \"centroid y\":HDCPy,\"countyNo\":countyNo,\"splitUnitNo\":splitUnitNo} )\n",
    "outname = STATE+str(int(nHDs))+\"_\"+str(nDistricts)+\"schoolVTDs_04.csv\"\n",
    "outpath = \"2024state_HD_output/\"+outname\n",
    "outDF.to_csv(outpath)  #muni-based complete 11-11-24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "164189ee-30cf-4cb2-80a9-22195eff585c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
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 },
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